Tag Archives: encoding

1834: The First Cyberattack

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/1834_the_first_.html

Tom Standage has a great story of the first cyberattack against a telegraph network.

The Blanc brothers traded government bonds at the exchange in the city of Bordeaux, where information about market movements took several days to arrive from Paris by mail coach. Accordingly, traders who could get the information more quickly could make money by anticipating these movements. Some tried using messengers and carrier pigeons, but the Blanc brothers found a way to use the telegraph line instead. They bribed the telegraph operator in the city of Tours to introduce deliberate errors into routine government messages being sent over the network.

The telegraph’s encoding system included a “backspace” symbol that instructed the transcriber to ignore the previous character. The addition of a spurious character indicating the direction of the previous day’s market movement, followed by a backspace, meant the text of the message being sent was unaffected when it was written out for delivery at the end of the line. But this extra character could be seen by another accomplice: a former telegraph operator who observed the telegraph tower outside Bordeaux with a telescope, and then passed on the news to the Blancs. The scam was only uncovered in 1836, when the crooked operator in Tours fell ill and revealed all to a friend, who he hoped would take his place. The Blanc brothers were put on trial, though they could not be convicted because there was no law against misuse of data networks. But the Blancs’ pioneering misuse of the French network qualifies as the world’s first cyber-attack.

EC2 Instance Update – C5 Instances with Local NVMe Storage (C5d)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/ec2-instance-update-c5-instances-with-local-nvme-storage-c5d/

As you can see from my EC2 Instance History post, we add new instance types on a regular and frequent basis. Driven by increasingly powerful processors and designed to address an ever-widening set of use cases, the size and diversity of this list reflects the equally diverse group of EC2 customers!

Near the bottom of that list you will find the new compute-intensive C5 instances. With a 25% to 50% improvement in price-performance over the C4 instances, the C5 instances are designed for applications like batch and log processing, distributed and or real-time analytics, high-performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding. Some of these applications can benefit from access to high-speed, ultra-low latency local storage. For example, video encoding, image manipulation, and other forms of media processing often necessitates large amounts of I/O to temporary storage. While the input and output files are valuable assets and are typically stored as Amazon Simple Storage Service (S3) objects, the intermediate files are expendable. Similarly, batch and log processing runs in a race-to-idle model, flushing volatile data to disk as fast as possible in order to make full use of compute resources.

New C5d Instances with Local Storage
In order to meet this need, we are introducing C5 instances equipped with local NVMe storage. Available for immediate use in 5 regions, these instances are a great fit for the applications that I described above, as well as others that you will undoubtedly dream up! Here are the specs:

Instance Name vCPUs RAM Local Storage EBS Bandwidth Network Bandwidth
c5d.large 2 4 GiB 1 x 50 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps
c5d.xlarge 4 8 GiB 1 x 100 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps
c5d.2xlarge 8 16 GiB 1 x 225 GB NVMe SSD Up to 2.25 Gbps Up to 10 Gbps
c5d.4xlarge 16 32 GiB 1 x 450 GB NVMe SSD 2.25 Gbps Up to 10 Gbps
c5d.9xlarge 36 72 GiB 1 x 900 GB NVMe SSD 4.5 Gbps 10 Gbps
c5d.18xlarge 72 144 GiB 2 x 900 GB NVMe SSD 9 Gbps 25 Gbps

Other than the addition of local storage, the C5 and C5d share the same specs. Both are powered by 3.0 GHz Intel Xeon Platinum 8000-series processors, optimized for EC2 and with full control over C-states on the two largest sizes, giving you the ability to run two cores at up to 3.5 GHz using Intel Turbo Boost Technology.

You can use any AMI that includes drivers for the Elastic Network Adapter (ENA) and NVMe; this includes the latest Amazon Linux, Microsoft Windows (Server 2008 R2, Server 2012, Server 2012 R2 and Server 2016), Ubuntu, RHEL, SUSE, and CentOS AMIs.

Here are a couple of things to keep in mind about the local NVMe storage:

Naming – You don’t have to specify a block device mapping in your AMI or during the instance launch; the local storage will show up as one or more devices (/dev/nvme*1 on Linux) after the guest operating system has booted.

Encryption – Each local NVMe device is hardware encrypted using the XTS-AES-256 block cipher and a unique key. Each key is destroyed when the instance is stopped or terminated.

Lifetime – Local NVMe devices have the same lifetime as the instance they are attached to, and do not stick around after the instance has been stopped or terminated.

Available Now
C5d instances are available in On-Demand, Reserved Instance, and Spot form in the US East (N. Virginia), US West (Oregon), EU (Ireland), US East (Ohio), and Canada (Central) Regions. Prices vary by Region, and are just a bit higher than for the equivalent C5 instances.

Jeff;

PS – We will be adding local NVMe storage to other EC2 instance types in the months to come, so stay tuned!

Pirates Taunt Amazon Over New “Turd Sandwich” Prime Video Quality

Post Syndicated from Andy original https://torrentfreak.com/pirates-taunt-amazon-over-new-turd-sandwich-prime-video-quality-180419/

Even though they generally aren’t paying for the content they consume, don’t fall into the trap of believing that all pirates are eternally grateful for even poor quality media.

Without a doubt, some of the most quality-sensitive individuals are to be found in pirate communities and they aren’t scared to make their voices known when release groups fail to come up with the best possible goods.

This week there’s been a sustained chorus of disapproval over the quality of pirate video releases sourced from Amazon Prime. The anger is usually directed at piracy groups who fail to capture content in the correct manner but according to a number of observers, the problem is actually at Amazon’s end.

Discussions on Reddit, for example, report that episodes in a single TV series have been declining in filesize and bitrate, from 1.56 GB in 720p at a 3073 kb/s video bitrate for episode 1, down to 907 MB in 720p at just 1514 kb/s video bitrate for episode 10.

Numerous theories as to why this may be the case are being floated around, including that Amazon is trying to save on bandwidth expenses. While this is a possibility, the company hasn’t made any announcements to that end.

Indeed, one legitimate customer reported that he’d raised the quality issue with Amazon and they’d said that the problem was “probably on his end”.

“I have Amazon Prime Video and I noticed the quality was always great for their exclusive shows, so I decided to try buying the shows on Amazon instead of iTunes this year. I paid for season pass subscriptions for Legion, Billions and Homeland this year,” he wrote.

“Just this past weekend, I have noticed a significant drop in details compared to weeks before! So naturally I assumed it was an issue on my end. I started trying different devices, calling support, etc, but nothing really helped.

“Billions continued to look like a blurry mess, almost like I was watching a standard definition DVD instead of the crystal clear HD I paid for and have experienced in the past! And when I check the previous episodes, sure enough, they look fantastic again. What the heck??”

With Amazon distancing itself from the issues, piracy groups have already begun to dig in the knife. Release group DEFLATE has been particularly critical.

“Amazon, in their infinite wisdom, have decided to start fucking with the quality of their encodes. They’re now reaching Netflix’s subpar 1080p.H264 levels, and their H265 encodes aren’t even close to what Netflix produces,” the group said in a file attached to S02E07 of The Good Fight released on Sunday.

“Netflix is able to produce drastic visual improvements with their H265 encodes compared to H264 across every original. In comparison, Amazon can’t decide whether H265 or H264 is going to produce better results, and as a result we suffer for it.”

Arrr! The quality be fallin’

So what’s happening exactly?

A TorrentFreak source (who tells us he’s been working in the BluRay/DCP authoring business for the last 10 years) was kind enough to give us two opinions, one aimed at the techies and another at us mere mortals.

“In technical terms, it appears [Amazon has] increased the CRF [Constant Rate Factor] value they use when encoding for both the HEVC [H265] and H264 streams. Previously, their H264 streams were using CRF 18 and a max bitrate of 15Mbit/s, which usually resulted in file sizes of roughly 3GB, or around 10Mbit/s. Similarly with their HEVC streams, they were using CRF 20 and resulting in streams which were around the same size,” he explained.

“In the past week, the H264 streams have decreased by up to 50% for some streams. While there are no longer any x264 headers embedded in the H264 streams, the HEVC streams still retain those headers and the CRF value used has been increased, so it does appear this change has been done on purpose.”

In layman’s terms, our source believes that Amazon had previously been using an encoding profile that was “right on the edge of relatively good quality” which kept bitrates relatively low but high enough to ensure no perceivable loss of quality.

“H264 streams encoded with CRF 18 could provide an acceptable compromise between quality and file size, where the loss of detail is often negligible when watched at regular viewing distances, at a desk, or in a lounge room on a larger TV,” he explained.

“Recently, it appears these values have been intentionally changed in order to lower the bitrate and file sizes for reasons unknown. As a result, the quality of some streams has been reduced by up to 50% of their previous values. This has introduced a visual loss of quality, comparable to that of viewing something in standard definition versus high definition.”

With the situation failing to improve during the week, by the time piracy group DEFLATE released S03E14 of Supergirl on Tuesday their original criticism had transformed into flat-out insults.

“These are only being done in H265 because Amazon have shit the bed, and it’s a choice between a turd sandwich and a giant douche,” they wrote, offering these images as illustrative of the problem and these indicating what should be achievable.

With DEFLATE advising customers to start complaining to Amazon, the memes have already begun, with unfavorable references to now-defunct group YIFY (which was often chastized for its low quality rips) and even a spin on one of the most well known anti-piracy campaigns.

You wouldn’t download stream….

TorrentFreak contacted Amazon Prime for comment on both the recent changes and growing customer complaints but at the time of publication we were yet to receive a response.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

Engineering deep dive: Encoding of SCTs in certificates

Post Syndicated from Let's Encrypt - Free SSL/TLS Certificates original https://letsencrypt.org/2018/04/04/sct-encoding.html

<p>Let&rsquo;s Encrypt recently <a href="https://community.letsencrypt.org/t/signed-certificate-timestamps-embedded-in-certificates/57187">launched SCT embedding in
certificates</a>.
This feature allows browsers to check that a certificate was submitted to a
<a href="https://en.wikipedia.org/wiki/Certificate_Transparency">Certificate Transparency</a>
log. As part of the launch, we did a thorough review
that the encoding of Signed Certificate Timestamps (SCTs) in our certificates
matches the relevant specifications. In this post, I&rsquo;ll dive into the details.
You&rsquo;ll learn more about X.509, ASN.1, DER, and TLS encoding, with references to
the relevant RFCs.</p>

<p>Certificate Transparency offers three ways to deliver SCTs to a browser: In a
TLS extension, in stapled OCSP, or embedded in a certificate. We chose to
implement the embedding method because it would just work for Let&rsquo;s Encrypt
subscribers without additional work. In the SCT embedding method, we submit
a &ldquo;precertificate&rdquo; with a <a href="#poison">poison extension</a> to a set of
CT logs, and get back SCTs. We then issue a real certificate based on the
precertificate, with two changes: The poison extension is removed, and the SCTs
obtained earlier are added in another extension.</p>

<p>Given a certificate, let&rsquo;s first look for the SCT list extension. According to CT (<a href="https://tools.ietf.org/html/rfc6962#section-3.3">RFC 6962
section 3.3</a>),
the extension OID for a list of SCTs is <code>1.3.6.1.4.1.11129.2.4.2</code>. An <a href="http://www.hl7.org/Oid/information.cfm">OID (object
ID)</a> is a series of integers, hierarchically
assigned and globally unique. They are used extensively in X.509, for instance
to uniquely identify extensions.</p>

<p>We can <a href="https://acme-v01.api.letsencrypt.org/acme/cert/031f2484307c9bc511b3123cb236a480d451">download an example certificate</a>,
and view it using OpenSSL (if your OpenSSL is old, it may not display the
detailed information):</p>

<pre><code>$ openssl x509 -noout -text -inform der -in Downloads/031f2484307c9bc511b3123cb236a480d451

CT Precertificate SCTs:
Signed Certificate Timestamp:
Version : v1(0)
Log ID : DB:74:AF:EE:CB:29:EC:B1:FE:CA:3E:71:6D:2C:E5:B9:
AA:BB:36:F7:84:71:83:C7:5D:9D:4F:37:B6:1F:BF:64
Timestamp : Mar 29 18:45:07.993 2018 GMT
Extensions: none
Signature : ecdsa-with-SHA256
30:44:02:20:7E:1F:CD:1E:9A:2B:D2:A5:0A:0C:81:E7:
13:03:3A:07:62:34:0D:A8:F9:1E:F2:7A:48:B3:81:76:
40:15:9C:D3:02:20:65:9F:E9:F1:D8:80:E2:E8:F6:B3:
25:BE:9F:18:95:6D:17:C6:CA:8A:6F:2B:12:CB:0F:55:
FB:70:F7:59:A4:19
Signed Certificate Timestamp:
Version : v1(0)
Log ID : 29:3C:51:96:54:C8:39:65:BA:AA:50:FC:58:07:D4:B7:
6F:BF:58:7A:29:72:DC:A4:C3:0C:F4:E5:45:47:F4:78
Timestamp : Mar 29 18:45:08.010 2018 GMT
Extensions: none
Signature : ecdsa-with-SHA256
30:46:02:21:00:AB:72:F1:E4:D6:22:3E:F8:7F:C6:84:
91:C2:08:D2:9D:4D:57:EB:F4:75:88:BB:75:44:D3:2F:
95:37:E2:CE:C1:02:21:00:8A:FF:C4:0C:C6:C4:E3:B2:
45:78:DA:DE:4F:81:5E:CB:CE:2D:57:A5:79:34:21:19:
A1:E6:5B:C7:E5:E6:9C:E2
</code></pre>

<p>Now let&rsquo;s go a little deeper. How is that extension represented in
the certificate? Certificates are expressed in
<a href="https://en.wikipedia.org/wiki/Abstract_Syntax_Notation_One">ASN.1</a>,
which generally refers to both a language for expressing data structures
and a set of formats for encoding them. The most common format,
<a href="https://en.wikipedia.org/wiki/X.690#DER_encoding">DER</a>,
is a tag-length-value format. That is, to encode an object, first you write
down a tag representing its type (usually one byte), then you write
down a number expressing how long the object is, then you write down
the object contents. This is recursive: An object can contain multiple
objects within it, each of which has its own tag, length, and value.</p>

<p>One of the cool things about DER and other tag-length-value formats is that you
can decode them to some degree without knowing what they mean. For instance, I
can tell you that 0x30 means the data type &ldquo;SEQUENCE&rdquo; (a struct, in ASN.1
terms), and 0x02 means &ldquo;INTEGER&rdquo;, then give you this hex byte sequence to
decode:</p>

<pre><code>30 06 02 01 03 02 01 0A
</code></pre>

<p>You could tell me right away that decodes to:</p>

<pre><code>SEQUENCE
INTEGER 3
INTEGER 10
</code></pre>

<p>Try it yourself with this great <a href="https://lapo.it/asn1js/#300602010302010A">JavaScript ASN.1
decoder</a>. However, you wouldn&rsquo;t know
what those integers represent without the corresponding ASN.1 schema (or
&ldquo;module&rdquo;). For instance, if you knew that this was a piece of DogData, and the
schema was:</p>

<pre><code>DogData ::= SEQUENCE {
legs INTEGER,
cutenessLevel INTEGER
}
</code></pre>

<p>You&rsquo;d know this referred to a three-legged dog with a cuteness level of 10.</p>

<p>We can take some of this knowledge and apply it to our certificates. As a first
step, convert the above certificate to hex with
<code>xxd -ps &lt; Downloads/031f2484307c9bc511b3123cb236a480d451</code>. You can then copy
and paste the result into
<a href="https://lapo.it/asn1js">lapo.it/asn1js</a> (or use <a href="https://lapo.it/asn1js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this handy link</a>). You can also run <code>openssl asn1parse -i -inform der -in Downloads/031f2484307c9bc511b3123cb236a480d451</code> to use OpenSSL&rsquo;s parser, which is less easy to use in some ways, but easier to copy and paste.</p>

<p>In the decoded data, we can find the OID <code>1.3.6.1.4.1.11129.2.4.2</code>, indicating
the SCT list extension. Per <a href="https://tools.ietf.org/html/rfc5280#page-17">RFC 5280, section
4.1</a>, an extension is defined:</p>

<pre><code>Extension ::= SEQUENCE {
extnID OBJECT IDENTIFIER,
critical BOOLEAN DEFAULT FALSE,
extnValue OCTET STRING
— contains the DER encoding of an ASN.1 value
— corresponding to the extension type identified
— by extnID
}
</code></pre>

<p>We&rsquo;ve found the <code>extnID</code>. The &ldquo;critical&rdquo; field is omitted because it has the
default value (false). Next up is the <code>extnValue</code>. This has the type
<code>OCTET STRING</code>, which has the tag &ldquo;0x04&rdquo;. <code>OCTET STRING</code> means &ldquo;here&rsquo;s
a bunch of bytes!&rdquo; In this case, as described by the spec, those bytes
happen to contain more DER. This is a fairly common pattern in X.509
to deal with parameterized data. For instance, this allows defining a
structure for extensions without knowing ahead of time all the structures
that a future extension might want to carry in its value. If you&rsquo;re a C
programmer, think of it as a <code>void*</code> for data structures. If you prefer Go,
think of it as an <code>interface{}</code>.</p>

<p>Here&rsquo;s that <code>extnValue</code>:</p>

<pre><code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
</code></pre>

<p>That&rsquo;s tag &ldquo;0x04&rdquo;, meaning <code>OCTET STRING</code>, followed by &ldquo;0x81 0xF5&rdquo;, meaning
&ldquo;this string is 245 bytes long&rdquo; (the 0x81 prefix is part of <a href="#variable-length">variable length
number encoding</a>).</p>

<p>According to <a href="https://tools.ietf.org/html/rfc6962#section-3.3">RFC 6962, section
3.3</a>, &ldquo;obtained SCTs
can be directly embedded in the final certificate, by encoding the
SignedCertificateTimestampList structure as an ASN.1 <code>OCTET STRING</code>
and inserting the resulting data in the TBSCertificate as an X.509v3
certificate extension&rdquo;</p>

<p>So, we have an <code>OCTET STRING</code>, all&rsquo;s good, right? Except if you remove the
tag and length from extnValue to get its value, you&rsquo;re left with:</p>

<pre><code>04 81 F2 00F0007500DB74AFEEC…
</code></pre>

<p>There&rsquo;s that &ldquo;0x04&rdquo; tag again, but with a shorter length. Why
do we nest one <code>OCTET STRING</code> inside another? It&rsquo;s because the
contents of extnValue are required by RFC 5280 to be valid DER, but a
SignedCertificateTimestampList is not encoded using DER (more on that
in a minute). So, by RFC 6962, a SignedCertificateTimestampList is wrapped in an
<code>OCTET STRING</code>, which is wrapped in another <code>OCTET STRING</code> (the extnValue).</p>

<p>Once we decode that second <code>OCTET STRING</code>, we&rsquo;re left with the contents:</p>

<pre><code>00F0007500DB74AFEEC…
</code></pre>

<p>&ldquo;0x00&rdquo; isn&rsquo;t a valid tag in DER. What is this? It&rsquo;s TLS encoding. This is
defined in <a href="https://tools.ietf.org/html/rfc5246#section-4">RFC 5246, section 4</a>
(the TLS 1.2 RFC). TLS encoding, like ASN.1, has both a way to define data
structures and a way to encode those structures. TLS encoding differs
from DER in that there are no tags, and lengths are only encoded when necessary for
variable-length arrays. Within an encoded structure, the type of a field is determined by
its position, rather than by a tag. This means that TLS-encoded structures are
more compact than DER structures, but also that they can&rsquo;t be processed without
knowing the corresponding schema. For instance, here&rsquo;s the top-level schema from
<a href="https://tools.ietf.org/html/rfc6962#section-3.3">RFC 6962, section 3.3</a>:</p>

<pre><code> The contents of the ASN.1 OCTET STRING embedded in an OCSP extension
or X509v3 certificate extension are as follows:

opaque SerializedSCT&lt;1..2^16-1&gt;;

struct {
SerializedSCT sct_list &lt;1..2^16-1&gt;;
} SignedCertificateTimestampList;

Here, &quot;SerializedSCT&quot; is an opaque byte string that contains the
serialized TLS structure.
</code></pre>

<p>Right away, we&rsquo;ve found one of those variable-length arrays. The length of such
an array (in bytes) is always represented by a length field just big enough to
hold the max array size. The max size of an <code>sct_list</code> is 65535 bytes, so the
length field is two bytes wide. Sure enough, those first two bytes are &ldquo;0x00
0xF0&rdquo;, or 240 in decimal. In other words, this <code>sct_list</code> will have 240 bytes. We
don&rsquo;t yet know how many SCTs will be in it. That will become clear only by
continuing to parse the encoded data and seeing where each struct ends (spoiler
alert: there are two SCTs!).</p>

<p>Now we know the first SerializedSCT starts with <code>0075…</code>. SerializedSCT
is itself a variable-length field, this time containing <code>opaque</code> bytes (much like <code>OCTET STRING</code>
back in the ASN.1 world). Like SignedCertificateTimestampList, it has a max size
of 65535 bytes, so we pull off the first two bytes and discover that the first
SerializedSCT is 0x0075 (117 decimal) bytes long. Here&rsquo;s the whole thing, in
hex:</p>

<pre><code>00DB74AFEECB29ECB1FECA3E716D2CE5B9AABB36F7847183C75D9D4F37B61FBF64000001627313EB19000004030046304402207E1FCD1E9A2BD2A50A0C81E713033A0762340DA8F91EF27A48B3817640159CD30220659FE9F1D880E2E8F6B325BE9F18956D17C6CA8A6F2B12CB0F55FB70F759A419
</code></pre>

<p>This can be decoded using the TLS encoding struct defined in <a href="https://tools.ietf.org/html/rfc6962#page-13">RFC 6962, section
3.2</a>:</p>

<pre><code>enum { v1(0), (255) }
Version;

struct {
opaque key_id[32];
} LogID;

opaque CtExtensions&lt;0..2^16-1&gt;;

struct {
Version sct_version;
LogID id;
uint64 timestamp;
CtExtensions extensions;
digitally-signed struct {
Version sct_version;
SignatureType signature_type = certificate_timestamp;
uint64 timestamp;
LogEntryType entry_type;
select(entry_type) {
case x509_entry: ASN.1Cert;
case precert_entry: PreCert;
} signed_entry;
CtExtensions extensions;
};
} SignedCertificateTimestamp;
</code></pre>

<p>Breaking that down:</p>

<pre><code># Version sct_version v1(0)
00
# LogID id (aka opaque key_id[32])
DB74AFEECB29ECB1FECA3E716D2CE5B9AABB36F7847183C75D9D4F37B61FBF64
# uint64 timestamp (milliseconds since the epoch)
000001627313EB19
# CtExtensions extensions (zero-length array)
0000
# digitally-signed struct
04030046304402207E1FCD1E9A2BD2A50A0C81E713033A0762340DA8F91EF27A48B3817640159CD30220659FE9F1D880E2E8F6B325BE9F18956D17C6CA8A6F2B12CB0F55FB70F759A419
</code></pre>

<p>To understand the &ldquo;digitally-signed struct,&rdquo; we need to turn back to <a href="https://tools.ietf.org/html/rfc5246#section-4.7">RFC 5246,
section 4.7</a>. It says:</p>

<pre><code>A digitally-signed element is encoded as a struct DigitallySigned:

struct {
SignatureAndHashAlgorithm algorithm;
opaque signature&lt;0..2^16-1&gt;;
} DigitallySigned;
</code></pre>

<p>And in <a href="https://tools.ietf.org/html/rfc5246#section-7.4.1.4.1">section
7.4.1.4.1</a>:</p>

<pre><code>enum {
none(0), md5(1), sha1(2), sha224(3), sha256(4), sha384(5),
sha512(6), (255)
} HashAlgorithm;

enum { anonymous(0), rsa(1), dsa(2), ecdsa(3), (255) }
SignatureAlgorithm;

struct {
HashAlgorithm hash;
SignatureAlgorithm signature;
} SignatureAndHashAlgorithm;
</code></pre>

<p>We have &ldquo;0x0403&rdquo;, which corresponds to sha256(4) and ecdsa(3). The next two
bytes, &ldquo;0x0046&rdquo;, tell us the length of the &ldquo;opaque signature&rdquo; field, 70 bytes in
decimal. To decode the signature, we reference <a href="https://tools.ietf.org/html/rfc4492#page-20">RFC 4492 section
5.4</a>, which says:</p>

<pre><code>The digitally-signed element is encoded as an opaque vector &lt;0..2^16-1&gt;, the
contents of which are the DER encoding corresponding to the
following ASN.1 notation.

Ecdsa-Sig-Value ::= SEQUENCE {
r INTEGER,
s INTEGER
}
</code></pre>

<p>Having dived through two layers of TLS encoding, we are now back in ASN.1 land!
We
<a href="https://lapo.it/asn1js/#304402207E1FCD1E9A2BD2A50A0C81E713033A0762340DA8F91EF27A48B3817640159CD30220659FE9F1D880E2E8F6B325BE9F18956D17C6CA8A6F2B12CB0F55FB70F759A419">decode</a>
the remaining bytes into a SEQUENCE containing two INTEGERS. And we&rsquo;re done! Here&rsquo;s the whole
extension decoded:</p>

<pre><code># Extension SEQUENCE – RFC 5280
30
# length 0x0104 bytes (260 decimal)
820104
# OBJECT IDENTIFIER
06
# length 0x0A bytes (10 decimal)
0A
# value (1.3.6.1.4.1.11129.2.4.2)
2B06010401D679020402
# OCTET STRING
04
# length 0xF5 bytes (245 decimal)
81F5
# OCTET STRING (embedded) – RFC 6962
04
# length 0xF2 bytes (242 decimal)
81F2
# Beginning of TLS encoded SignedCertificateTimestampList – RFC 5246 / 6962
# length 0xF0 bytes
00F0
# opaque SerializedSCT&lt;1..2^16-1&gt;
# length 0x75 bytes
0075
# Version sct_version v1(0)
00
# LogID id (aka opaque key_id[32])
DB74AFEECB29ECB1FECA3E716D2CE5B9AABB36F7847183C75D9D4F37B61FBF64
# uint64 timestamp (milliseconds since the epoch)
000001627313EB19
# CtExtensions extensions (zero-length array)
0000
# digitally-signed struct – RFC 5426
# SignatureAndHashAlgorithm (ecdsa-sha256)
0403
# opaque signature&lt;0..2^16-1&gt;;
# length 0x0046
0046
# DER-encoded Ecdsa-Sig-Value – RFC 4492
30 # SEQUENCE
44 # length 0x44 bytes
02 # r INTEGER
20 # length 0x20 bytes
# value
7E1FCD1E9A2BD2A50A0C81E713033A0762340DA8F91EF27A48B3817640159CD3
02 # s INTEGER
20 # length 0x20 bytes
# value
659FE9F1D880E2E8F6B325BE9F18956D17C6CA8A6F2B12CB0F55FB70F759A419
# opaque SerializedSCT&lt;1..2^16-1&gt;
# length 0x77 bytes
0077
# Version sct_version v1(0)
00
# LogID id (aka opaque key_id[32])
293C519654C83965BAAA50FC5807D4B76FBF587A2972DCA4C30CF4E54547F478
# uint64 timestamp (milliseconds since the epoch)
000001627313EB2A
# CtExtensions extensions (zero-length array)
0000
# digitally-signed struct – RFC 5426
# SignatureAndHashAlgorithm (ecdsa-sha256)
0403
# opaque signature&lt;0..2^16-1&gt;;
# length 0x0048
0048
# DER-encoded Ecdsa-Sig-Value – RFC 4492
30 # SEQUENCE
46 # length 0x46 bytes
02 # r INTEGER
21 # length 0x21 bytes
# value
00AB72F1E4D6223EF87FC68491C208D29D4D57EBF47588BB7544D32F9537E2CEC1
02 # s INTEGER
21 # length 0x21 bytes
# value
008AFFC40CC6C4E3B24578DADE4F815ECBCE2D57A579342119A1E65BC7E5E69CE2
</code></pre>

<p>One surprising thing you might notice: In the first SCT, <code>r</code> and <code>s</code> are twenty
bytes long. In the second SCT, they are both twenty-one bytes long, and have a
leading zero. Integers in DER are two&rsquo;s complement, so if the leftmost bit is
set, they are interpreted as negative. Since <code>r</code> and <code>s</code> are positive, if the
leftmost bit would be a 1, an extra byte has to be added so that the leftmost
bit can be 0.</p>

<p>This is a little taste of what goes into encoding a certificate. I hope it was
informative! If you&rsquo;d like to learn more, I recommend &ldquo;<a href="http://luca.ntop.org/Teaching/Appunti/asn1.html">A Layman&rsquo;s Guide to a
Subset of ASN.1, BER, and DER</a>.&rdquo;</p>

<p><a name="poison"></a>Footnote 1: A &ldquo;poison extension&rdquo; is defined by <a href="https://tools.ietf.org/html/rfc6962#section-3.1">RFC 6962
section 3.1</a>:</p>

<pre><code>The Precertificate is constructed from the certificate to be issued by adding a special
critical poison extension (OID `1.3.6.1.4.1.11129.2.4.3`, whose
extnValue OCTET STRING contains ASN.1 NULL data (0x05 0x00))
</code></pre>

<p>In other words, it&rsquo;s an empty extension whose only purpose is to ensure that
certificate processors will not accept precertificates as valid certificates. The
specification ensures this by setting the &ldquo;critical&rdquo; bit on the extension, which
ensures that code that doesn&rsquo;t recognize the extension will reject the whole
certificate. Code that does recognize the extension specifically as poison
will also reject the certificate.</p>

<p><a name="variable-length"></a>Footnote 2: Lengths from 0-127 are represented by
a single byte (short form). To express longer lengths, more bytes are used (long form).
The high bit (0x80) on the first byte is set to distinguish long form from short
form. The remaining bits are used to express how many more bytes to read for the
length. For instance, 0x81F5 means &ldquo;this is long form because the length is
greater than 127, but there&rsquo;s still only one byte of length (0xF5) to decode.&rdquo;</p>

Dolby Labs Sues Adobe For Copyright Infringement

Post Syndicated from Andy original https://torrentfreak.com/dolby-labs-sues-adobe-for-copyright-infringement-180314/

Adobe has some of the most recognized software products on the market today, including Photoshop which has become a household name.

While the company has been subjected to more than its fair share of piracy over the years, a new lawsuit accuses the software giant itself of infringement.

Dolby Laboratories is best known as a company specializing in noise reduction and audio encoding and compression technologies. Its reversed double ‘D’ logo is widely recognized after appearing on millions of home hi-fi systems and film end credits.

In a complaint filed this week at a federal court in California, Dolby Labs alleges that after supplying its products to Adobe for 15 years, the latter has failed to live up to its licensing obligations and is guilty of copyright infringement and breach of contract.

“Between 2002 and 2017, Adobe designed and sold its audio-video content creation and editing software with Dolby’s industry-leading audio processing technologies,” Dolby’s complaint reads.

“The basic terms of Adobe’s licenses for products containing Dolby technologies are clear; when Adobe granted its customer a license to any Adobe product that contained Dolby technology, Adobe was contractually obligated to report the sale to Dolby and pay the agreed-upon royalty.”

Dolby says that Adobe promised it wouldn’t sell its any of its products (such as Audition, After Effects, Encore, Lightroom, and Premiere Pro) outside the scope of its licenses with Dolby. Those licenses included clauses which grant Dolby the right to inspect Adobe’s records through a third-party audit, in order to verify the accuracy of Adobe’s sales reporting and associated payment of royalties.

Over the past several years, however, things didn’t go to plan. The lawsuit claims that when Dolby tried to audit Adobe’s books, Adobe refused to “engage in even basic auditing and information sharing practices,” a rather ironic situation given the demands that Adobe places on its own licensees.

Dolby’s assessment is that Adobe spent years withholding this information in an effort to hide the full scale of its non-compliance.

“The limited information that Dolby has reviewed to-date demonstrates that Adobe included Dolby technologies in numerous Adobe software products and collections of products, but refused to report each sale or pay the agreed-upon royalties owed to Dolby,” the lawsuit claims.

Due to the lack of information in Dolby’s possession, the company says it cannot determine the full scope of Adobe’s infringement. However, Dolby accuses Adobe of multiple breaches including bundling licensed products together but only reporting one sale, selling multiple products to one customer but only paying a single license, failing to pay licenses on product upgrades, and even selling products containing Dolby technology without paying a license at all.

Dolby entered into licensing agreements with Adobe in 2003, 2012 and 2013, with each agreement detailing payment of royalties by Adobe to Dolby for each product licensed to Adobe’s customers containing Dolby technology. In the early days when the relationship between the companies first began, Adobe sold either a physical product in “shrink-wrap” form or downloads from its website, a position which made reporting very easy.

In late 2011, however, Adobe began its transition to offering its Creative Cloud (SaaS model) under which customers purchase a subscription to access Adobe software, some of which contains Dolby technology. Depending on how much the customer pays, users can select up to thirty Adobe products. At this point, things appear to have become much more complex.

On January 15, 2015, Dolby tried to inspect Adobe’s books for the period 2012-2014 via a third-party auditing firm. But, according to Dolby, over the next three years “Adobe employed various tactics to frustrate Dolby’s right to audit Adobe’s inclusion of Dolby Technologies in Adobe’s products.”

Dolby points out that under Adobe’s own licensing conditions, businesses must allow Adobe’s auditors to allow the company to inspect their records on seven days’ notice to confirm they are not in breach of Adobe licensing terms. Any discovered shortfalls in licensing must then be paid for, at a rate higher than the original license. This, Dolby says, shows that Adobe is clearly aware of why and how auditing takes place.

“After more than three years of attempting to audit Adobe’s Sales of products containing Dolby Technologies, Dolby still has not received the information required to complete an audit for the full time period,” Dolby explains.

But during this period, Adobe didn’t stand still. According to Dolby, Adobe tried to obtain new licensing from Dolby at a lower price. Dolby stood its ground and insisted on an audit first but despite an official demand, Adobe didn’t provide the complete set of books and records requested.

Eventually, Dolby concluded that Adobe had “no intention to fully comply with its audit obligations” so called in its lawyers to deal with the matter.

“Adobe’s direct and induced infringements of Dolby Licensing’s copyrights in the Asserted Dolby Works are and have been knowing, deliberate, and willful. By its unauthorized copying, use, and distribution of the Asserted Dolby Works and the Adobe Infringing Products, Adobe has violated Dolby Licensing’s exclusive rights..,” the lawsuit reads.

Noting that Adobe has profited and gained a commercial advantage as a result of its alleged infringement, Dolby demands injunctive relief restraining the company from any further breaches in violation of US copyright law.

“Dolby now brings this action to protect its intellectual property, maintain fairness across its licensing partnerships, and to fund the next generations of technology that empower the creative community which Dolby serves,” the company concludes.

Dolby’s full complaint can be found here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Detecting Drone Surveillance with Traffic Analysis

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/detecting_drone.html

This is clever:

Researchers at Ben Gurion University in Beer Sheva, Israel have built a proof-of-concept system for counter-surveillance against spy drones that demonstrates a clever, if not exactly simple, way to determine whether a certain person or object is under aerial surveillance. They first generate a recognizable pattern on whatever subject­ — a window, say — someone might want to guard from potential surveillance. Then they remotely intercept a drone’s radio signals to look for that pattern in the streaming video the drone sends back to its operator. If they spot it, they can determine that the drone is looking at their subject.

In other words, they can see what the drone sees, pulling out their recognizable pattern from the radio signal, even without breaking the drone’s encrypted video.

The details have to do with the way drone video is compressed:

The researchers’ technique takes advantage of an efficiency feature streaming video has used for years, known as “delta frames.” Instead of encoding video as a series of raw images, it’s compressed into a series of changes from the previous image in the video. That means when a streaming video shows a still object, it transmits fewer bytes of data than when it shows one that moves or changes color.

That compression feature can reveal key information about the content of the video to someone who’s intercepting the streaming data, security researchers have shown in recent research, even when the data is encrypted.

Research paper and video.

Optimize Delivery of Trending, Personalized News Using Amazon Kinesis and Related Services

Post Syndicated from Yukinori Koide original https://aws.amazon.com/blogs/big-data/optimize-delivery-of-trending-personalized-news-using-amazon-kinesis-and-related-services/

This is a guest post by Yukinori Koide, an the head of development for the Newspass department at Gunosy.

Gunosy is a news curation application that covers a wide range of topics, such as entertainment, sports, politics, and gourmet news. The application has been installed more than 20 million times.

Gunosy aims to provide people with the content they want without the stress of dealing with a large influx of information. We analyze user attributes, such as gender and age, and past activity logs like click-through rate (CTR). We combine this information with article attributes to provide trending, personalized news articles to users.

In this post, I show you how to process user activity logs in real time using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services.

Why does Gunosy need real-time processing?

Users need fresh and personalized news. There are two constraints to consider when delivering appropriate articles:

  • Time: Articles have freshness—that is, they lose value over time. New articles need to reach users as soon as possible.
  • Frequency (volume): Only a limited number of articles can be shown. It’s unreasonable to display all articles in the application, and users can’t read all of them anyway.

To deliver fresh articles with a high probability that the user is interested in them, it’s necessary to include not only past user activity logs and some feature values of articles, but also the most recent (real-time) user activity logs.

We optimize the delivery of articles with these two steps.

  1. Personalization: Deliver articles based on each user’s attributes, past activity logs, and feature values of each article—to account for each user’s interests.
  2. Trends analysis/identification: Optimize delivering articles using recent (real-time) user activity logs—to incorporate the latest trends from all users.

Optimizing the delivery of articles is always a cold start. Initially, we deliver articles based on past logs. We then use real-time data to optimize as quickly as possible. In addition, news has a short freshness time. Specifically, day-old news is past news, and even the news that is three hours old is past news. Therefore, shortening the time between step 1 and step 2 is important.

To tackle this issue, we chose AWS for processing streaming data because of its fully managed services, cost-effectiveness, and so on.

Solution

The following diagrams depict the architecture for optimizing article delivery by processing real-time user activity logs

There are three processing flows:

  1. Process real-time user activity logs.
  2. Store and process all user-based and article-based logs.
  3. Execute ad hoc or heavy queries.

In this post, I focus on the first processing flow and explain how it works.

Process real-time user activity logs

The following are the steps for processing user activity logs in real time using Kinesis Data Streams and Kinesis Data Analytics.

  1. The Fluentd server sends the following user activity logs to Kinesis Data Streams:
{"article_id": 12345, "user_id": 12345, "action": "click"}
{"article_id": 12345, "user_id": 12345, "action": "impression"}
...
  1. Map rows of logs to columns in Kinesis Data Analytics.

  1. Set the reference data to Kinesis Data Analytics from Amazon S3.

a. Gunosy has user attributes such as gender, age, and segment. Prepare the following CSV file (user_id, gender, segment_id) and put it in Amazon S3:

101,female,1
102,male,2
103,female,3
...

b. Add the application reference data source to Kinesis Data Analytics using the AWS CLI:

$ aws kinesisanalytics add-application-reference-data-source \
  --application-name <my-application-name> \
  --current-application-version-id <version-id> \
  --reference-data-source '{
  "TableName": "REFERENCE_DATA_SOURCE",
  "S3ReferenceDataSource": {
    "BucketARN": "arn:aws:s3:::<my-bucket-name>",
    "FileKey": "mydata.csv",
    "ReferenceRoleARN": "arn:aws:iam::<account-id>:role/..."
  },
  "ReferenceSchema": {
    "RecordFormat": {
      "RecordFormatType": "CSV",
      "MappingParameters": {
        "CSVMappingParameters": {"RecordRowDelimiter": "\n", "RecordColumnDelimiter": ","}
      }
    },
    "RecordEncoding": "UTF-8",
    "RecordColumns": [
      {"Name": "USER_ID", "Mapping": "0", "SqlType": "INTEGER"},
      {"Name": "GENDER",  "Mapping": "1", "SqlType": "VARCHAR(32)"},
      {"Name": "SEGMENT_ID", "Mapping": "2", "SqlType": "INTEGER"}
    ]
  }
}'

This application reference data source can be referred on Kinesis Data Analytics.

  1. Run a query against the source data stream on Kinesis Data Analytics with the application reference data source.

a. Define the temporary stream named TMP_SQL_STREAM.

CREATE OR REPLACE STREAM "TMP_SQL_STREAM" (
  GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER
);

b. Insert the joined source stream and application reference data source into the temporary stream.

CREATE OR REPLACE PUMP "TMP_PUMP" AS
INSERT INTO "TMP_SQL_STREAM"
SELECT STREAM
  R.GENDER, R.SEGMENT_ID, S.ARTICLE_ID, S.ACTION
FROM      "SOURCE_SQL_STREAM_001" S
LEFT JOIN "REFERENCE_DATA_SOURCE" R
  ON S.USER_ID = R.USER_ID;

c. Define the destination stream named DESTINATION_SQL_STREAM.

CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
  TIME TIMESTAMP, GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER, 
  IMPRESSION INTEGER, CLICK INTEGER
);

d. Insert the processed temporary stream, using a tumbling window, into the destination stream per minute.

CREATE OR REPLACE PUMP "STREAM_PUMP" AS
INSERT INTO "DESTINATION_SQL_STREAM"
SELECT STREAM
  ROW_TIME AS TIME,
  GENDER, SEGMENT_ID, ARTICLE_ID,
  SUM(CASE ACTION WHEN 'impression' THEN 1 ELSE 0 END) AS IMPRESSION,
  SUM(CASE ACTION WHEN 'click' THEN 1 ELSE 0 END) AS CLICK
FROM "TMP_SQL_STREAM"
GROUP BY
  GENDER, SEGMENT_ID, ARTICLE_ID,
  FLOOR("TMP_SQL_STREAM".ROWTIME TO MINUTE);

The results look like the following:

  1. Insert the results into Amazon Elasticsearch Service (Amazon ES).
  2. Batch servers get results from Amazon ES every minute. They then optimize delivering articles with other data sources using a proprietary optimization algorithm.

How to connect a stream to another stream in another AWS Region

When we built the solution, Kinesis Data Analytics was not available in the Asia Pacific (Tokyo) Region, so we used the US West (Oregon) Region. The following shows how we connected a data stream to another data stream in the other Region.

There is no need to continue containing all components in a single AWS Region, unless you have a situation where a response difference at the millisecond level is critical to the service.

Benefits

The solution provides benefits for both our company and for our users. Benefits for the company are cost savings—including development costs, operational costs, and infrastructure costs—and reducing delivery time. Users can now find articles of interest more quickly. The solution can process more than 500,000 records per minute, and it enables fast and personalized news curating for our users.

Conclusion

In this post, I showed you how we optimize trending user activities to personalize news using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services in Gunosy.

AWS gives us a quick and economical solution and a good experience.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Implement Serverless Log Analytics Using Amazon Kinesis Analytics and Joining and Enriching Streaming Data on Amazon Kinesis.


About the Authors

Yukinori Koide is the head of development for the Newspass department at Gunosy. He is working on standardization of provisioning and deployment flow, promoting the utilization of serverless and containers for machine learning and AI services. His favorite AWS services are DynamoDB, Lambda, Kinesis, and ECS.

 

 

 

Akihiro Tsukada is a start-up solutions architect with AWS. He supports start-up companies in Japan technically at many levels, ranging from seed to later-stage.

 

 

 

 

Yuta Ishii is a solutions architect with AWS. He works with our customers to provide architectural guidance for building media & entertainment services, helping them improve the value of their services when using AWS.

 

 

 

 

 

How to Enhance the Security of Sensitive Customer Data by Using Amazon CloudFront Field-Level Encryption

Post Syndicated from Alex Tomic original https://aws.amazon.com/blogs/security/how-to-enhance-the-security-of-sensitive-customer-data-by-using-amazon-cloudfront-field-level-encryption/

Amazon CloudFront is a web service that speeds up distribution of your static and dynamic web content to end users through a worldwide network of edge locations. CloudFront provides a number of benefits and capabilities that can help you secure your applications and content while meeting compliance requirements. For example, you can configure CloudFront to help enforce secure, end-to-end connections using HTTPS SSL/TLS encryption. You also can take advantage of CloudFront integration with AWS Shield for DDoS protection and with AWS WAF (a web application firewall) for protection against application-layer attacks, such as SQL injection and cross-site scripting.

Now, CloudFront field-level encryption helps secure sensitive data such as a customer phone numbers by adding another security layer to CloudFront HTTPS. Using this functionality, you can help ensure that sensitive information in a POST request is encrypted at CloudFront edge locations. This information remains encrypted as it flows to and beyond your origin servers that terminate HTTPS connections with CloudFront and throughout the application environment. In this blog post, we demonstrate how you can enhance the security of sensitive data by using CloudFront field-level encryption.

Note: This post assumes that you understand concepts and services such as content delivery networks, HTTP forms, public-key cryptography, CloudFrontAWS Lambda, and the AWS CLI. If necessary, you should familiarize yourself with these concepts and review the solution overview in the next section before proceeding with the deployment of this post’s solution.

How field-level encryption works

Many web applications collect and store data from users as those users interact with the applications. For example, a travel-booking website may ask for your passport number and less sensitive data such as your food preferences. This data is transmitted to web servers and also might travel among a number of services to perform tasks. However, this also means that your sensitive information may need to be accessed by only a small subset of these services (most other services do not need to access your data).

User data is often stored in a database for retrieval at a later time. One approach to protecting stored sensitive data is to configure and code each service to protect that sensitive data. For example, you can develop safeguards in logging functionality to ensure sensitive data is masked or removed. However, this can add complexity to your code base and limit performance.

Field-level encryption addresses this problem by ensuring sensitive data is encrypted at CloudFront edge locations. Sensitive data fields in HTTPS form POSTs are automatically encrypted with a user-provided public RSA key. After the data is encrypted, other systems in your architecture see only ciphertext. If this ciphertext unintentionally becomes externally available, the data is cryptographically protected and only designated systems with access to the private RSA key can decrypt the sensitive data.

It is critical to secure private RSA key material to prevent unauthorized access to the protected data. Management of cryptographic key material is a larger topic that is out of scope for this blog post, but should be carefully considered when implementing encryption in your applications. For example, in this blog post we store private key material as a secure string in the Amazon EC2 Systems Manager Parameter Store. The Parameter Store provides a centralized location for managing your configuration data such as plaintext data (such as database strings) or secrets (such as passwords) that are encrypted using AWS Key Management Service (AWS KMS). You may have an existing key management system in place that you can use, or you can use AWS CloudHSM. CloudHSM is a cloud-based hardware security module (HSM) that enables you to easily generate and use your own encryption keys in the AWS Cloud.

To illustrate field-level encryption, let’s look at a simple form submission where Name and Phone values are sent to a web server using an HTTP POST. A typical form POST would contain data such as the following.

POST / HTTP/1.1
Host: example.com
Content-Type: application/x-www-form-urlencoded
Content-Length:60

Name=Jane+Doe&Phone=404-555-0150

Instead of taking this typical approach, field-level encryption converts this data similar to the following.

POST / HTTP/1.1
Host: example.com
Content-Type: application/x-www-form-urlencoded
Content-Length: 1713

Name=Jane+Doe&Phone=AYABeHxZ0ZqWyysqxrB5pEBSYw4AAA...

To further demonstrate field-level encryption in action, this blog post includes a sample serverless application that you can deploy by using a CloudFormation template, which creates an application environment using CloudFront, Amazon API Gateway, and Lambda. The sample application is only intended to demonstrate field-level encryption functionality and is not intended for production use. The following diagram depicts the architecture and data flow of this sample application.

Sample application architecture and data flow

Diagram of the solution's architecture and data flow

Here is how the sample solution works:

  1. An application user submits an HTML form page with sensitive data, generating an HTTPS POST to CloudFront.
  2. Field-level encryption intercepts the form POST and encrypts sensitive data with the public RSA key and replaces fields in the form post with encrypted ciphertext. The form POST ciphertext is then sent to origin servers.
  3. The serverless application accepts the form post data containing ciphertext where sensitive data would normally be. If a malicious user were able to compromise your application and gain access to your data, such as the contents of a form, that user would see encrypted data.
  4. Lambda stores data in a DynamoDB table, leaving sensitive data to remain safely encrypted at rest.
  5. An administrator uses the AWS Management Console and a Lambda function to view the sensitive data.
  6. During the session, the administrator retrieves ciphertext from the DynamoDB table.
  7. The administrator decrypts sensitive data by using private key material stored in the EC2 Systems Manager Parameter Store.
  8. Decrypted sensitive data is transmitted over SSL/TLS via the AWS Management Console to the administrator for review.

Deployment walkthrough

The high-level steps to deploy this solution are as follows:

  1. Stage the required artifacts
    When deployment packages are used with Lambda, the zipped artifacts have to be placed in an S3 bucket in the target AWS Region for deployment. This step is not required if you are deploying in the US East (N. Virginia) Region because the package has already been staged there.
  2. Generate an RSA key pair
    Create a public/private key pair that will be used to perform the encrypt/decrypt functionality.
  3. Upload the public key to CloudFront and associate it with the field-level encryption configuration
    After you create the key pair, the public key is uploaded to CloudFront so that it can be used by field-level encryption.
  4. Launch the CloudFormation stack
    Deploy the sample application for demonstrating field-level encryption by using AWS CloudFormation.
  5. Add the field-level encryption configuration to the CloudFront distribution
    After you have provisioned the application, this step associates the field-level encryption configuration with the CloudFront distribution.
  6. Store the RSA private key in the Parameter Store
    Store the private key in the Parameter Store as a SecureString data type, which uses AWS KMS to encrypt the parameter value.

Deploy the solution

1. Stage the required artifacts

(If you are deploying in the US East [N. Virginia] Region, skip to Step 2, “Generate an RSA key pair.”)

Stage the Lambda function deployment package in an Amazon S3 bucket located in the AWS Region you are using for this solution. To do this, download the zipped deployment package and upload it to your in-region bucket. For additional information about uploading objects to S3, see Uploading Object into Amazon S3.

2. Generate an RSA key pair

In this section, you will generate an RSA key pair by using OpenSSL:

  1. Confirm access to OpenSSL.
    $ openssl version

    You should see version information similar to the following.

    OpenSSL <version> <date>

  1. Create a private key using the following command.
    $ openssl genrsa -out private_key.pem 2048

    The command results should look similar to the following.

    Generating RSA private key, 2048 bit long modulus
    ................................................................................+++
    ..........................+++
    e is 65537 (0x10001)
  1. Extract the public key from the private key by running the following command.
    $ openssl rsa -pubout -in private_key.pem -out public_key.pem

    You should see output similar to the following.

    writing RSA key
  1. Restrict access to the private key.$ chmod 600 private_key.pem Note: You will use the public and private key material in Steps 3 and 6 to configure the sample application.

3. Upload the public key to CloudFront and associate it with the field-level encryption configuration

Now that you have created the RSA key pair, you will use the AWS Management Console to upload the public key to CloudFront for use by field-level encryption. Complete the following steps to upload and configure the public key.

Note: Do not include spaces or special characters when providing the configuration values in this section.

  1. From the AWS Management Console, choose Services > CloudFront.
  2. In the navigation pane, choose Public Key and choose Add Public Key.
    Screenshot of adding a public key

Complete the Add Public Key configuration boxes:

  • Key Name: Type a name such as DemoPublicKey.
  • Encoded Key: Paste the contents of the public_key.pem file you created in Step 2c. Copy and paste the encoded key value for your public key, including the -----BEGIN PUBLIC KEY----- and -----END PUBLIC KEY----- lines.
  • Comment: Optionally add a comment.
  1. Choose Create.
  2. After adding at least one public key to CloudFront, the next step is to create a profile to tell CloudFront which fields of input you want to be encrypted. While still on the CloudFront console, choose Field-level encryption in the navigation pane.
  3. Under Profiles, choose Create profile.
    Screenshot of creating a profile

Complete the Create profile configuration boxes:

  • Name: Type a name such as FLEDemo.
  • Comment: Optionally add a comment.
  • Public key: Select the public key you configured in Step 4.b.
  • Provider name: Type a provider name such as FLEDemo.
    This information will be used when the form data is encrypted, and must be provided to applications that need to decrypt the data, along with the appropriate private key.
  • Pattern to match: Type phone. This configures field-level encryption to match based on the phone.
  1. Choose Save profile.
  2. Configurations include options for whether to block or forward a query to your origin in scenarios where CloudFront can’t encrypt the data. Under Encryption Configurations, choose Create configuration.
    Screenshot of creating a configuration

Complete the Create configuration boxes:

  • Comment: Optionally add a comment.
  • Content type: Enter application/x-www-form-urlencoded. This is a common media type for encoding form data.
  • Default profile ID: Select the profile you added in Step 3e.
  1. Choose Save configuration

4. Launch the CloudFormation stack

Launch the sample application by using a CloudFormation template that automates the provisioning process.

Input parameter Input parameter description
ProviderID Enter the Provider name you assigned in Step 3e. The ProviderID is used in field-level encryption configuration in CloudFront (letters and numbers only, no special characters)
PublicKeyName Enter the Key Name you assigned in Step 3b. This name is assigned to the public key in field-level encryption configuration in CloudFront (letters and numbers only, no special characters).
PrivateKeySSMPath Leave as the default: /cloudfront/field-encryption-sample/private-key
ArtifactsBucket The S3 bucket with artifact files (staged zip file with app code). Leave as default if deploying in us-east-1.
ArtifactsPrefix The path in the S3 bucket containing artifact files. Leave as default if deploying in us-east-1.

To finish creating the CloudFormation stack:

  1. Choose Next on the Select Template page, enter the input parameters and choose Next.
    Note: The Artifacts configuration needs to be updated only if you are deploying outside of us-east-1 (US East [N. Virginia]). See Step 1 for artifact staging instructions.
  2. On the Options page, accept the defaults and choose Next.
  3. On the Review page, confirm the details, choose the I acknowledge that AWS CloudFormation might create IAM resources check box, and then choose Create. (The stack will be created in approximately 15 minutes.)

5. Add the field-level encryption configuration to the CloudFront distribution

While still on the CloudFront console, choose Distributions in the navigation pane, and then:

    1. In the Outputs section of the FLE-Sample-App stack, look for CloudFrontDistribution and click the URL to open the CloudFront console.
    2. Choose Behaviors, choose the Default (*) behavior, and then choose Edit.
    3. For Field-level Encryption Config, choose the configuration you created in Step 3g.
      Screenshot of editing the default cache behavior
    4. Choose Yes, Edit.
    5. While still in the CloudFront distribution configuration, choose the General Choose Edit, scroll down to Distribution State, and change it to Enabled.
    6. Choose Yes, Edit.

6. Store the RSA private key in the Parameter Store

In this step, you store the private key in the EC2 Systems Manager Parameter Store as a SecureString data type, which uses AWS KMS to encrypt the parameter value. For more information about AWS KMS, see the AWS Key Management Service Developer Guide. You will need a working installation of the AWS CLI to complete this step.

  1. Store the private key in the Parameter Store with the AWS CLI by running the following command. You will find the <KMSKeyID> in the KMSKeyID in the CloudFormation stack Outputs. Substitute it for the placeholder in the following command.
    $ aws ssm put-parameter --type "SecureString" --name /cloudfront/field-encryption-sample/private-key --value file://private_key.pem --key-id "<KMSKeyID>"
    
    ------------------
    |  PutParameter  |
    +----------+-----+
    |  Version |  1  |
    +----------+-----+

  1. Verify the parameter. Your private key material should be accessible through the ssm get-parameter in the following command in the Value The key material has been truncated in the following output.
    $ aws ssm get-parameter --name /cloudfront/field-encryption-sample/private-key --with-decryption
    
    -----…
    
    ||  Value  |  -----BEGIN RSA PRIVATE KEY-----
    MIIEowIBAAKCAQEAwGRBGuhacmw+C73kM6Z…….

    Notice we use the —with decryption argument in this command. This returns the private key as cleartext.

    This completes the sample application deployment. Next, we show you how to see field-level encryption in action.

  1. Delete the private key from local storage. On Linux for example, using the shred command, securely delete the private key material from your workstation as shown below. You may also wish to store the private key material within an AWS CloudHSM or other protected location suitable for your security requirements. For production implementations, you also should implement key rotation policies.
    $ shred -zvu -n  100 private*.pem
    
    shred: private_encrypted_key.pem: pass 1/101 (random)...
    shred: private_encrypted_key.pem: pass 2/101 (dddddd)...
    shred: private_encrypted_key.pem: pass 3/101 (555555)...
    ….

Test the sample application

Use the following steps to test the sample application with field-level encryption:

  1. Open sample application in your web browser by clicking the ApplicationURL link in the CloudFormation stack Outputs. (for example, https:d199xe5izz82ea.cloudfront.net/prod/). Note that it may take several minutes for the CloudFront distribution to reach the Deployed Status from the previous step, during which time you may not be able to access the sample application.
  2. Fill out and submit the HTML form on the page:
    1. Complete the three form fields: Full Name, Email Address, and Phone Number.
    2. Choose Submit.
      Screenshot of completing the sample application form
      Notice that the application response includes the form values. The phone number returns the following ciphertext encryption using your public key. This ciphertext has been stored in DynamoDB.
      Screenshot of the phone number as ciphertext
  3. Execute the Lambda decryption function to download ciphertext from DynamoDB and decrypt the phone number using the private key:
    1. In the CloudFormation stack Outputs, locate DecryptFunction and click the URL to open the Lambda console.
    2. Configure a test event using the “Hello World” template.
    3. Choose the Test button.
  4. View the encrypted and decrypted phone number data.
    Screenshot of the encrypted and decrypted phone number data

Summary

In this blog post, we showed you how to use CloudFront field-level encryption to encrypt sensitive data at edge locations and help prevent access from unauthorized systems. The source code for this solution is available on GitHub. For additional information about field-level encryption, see the documentation.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, please start a new thread on the CloudFront forum.

– Alex and Cameron

Is blockchain a security topic? (Opensource.com)

Post Syndicated from jake original https://lwn.net/Articles/740929/rss

At Opensource.com, Mike Bursell looks at blockchain security from the angle of trust. Unlike cryptocurrencies, which are pseudonymous typically, other kinds of blockchains will require mapping users to real-life identities; that raises the trust issue.

What’s really interesting is that, if you’re thinking about moving to a permissioned blockchain or distributed ledger with permissioned actors, then you’re going to have to spend some time thinking about trust. You’re unlikely to be using a proof-of-work system for making blocks—there’s little point in a permissioned system—so who decides what comprises a “valid” block that the rest of the system should agree on? Well, you can rotate around some (or all) of the entities, or you can have a random choice, or you can elect a small number of über-trusted entities. Combinations of these schemes may also work.

If these entities all exist within one trust domain, which you control, then fine, but what if they’re distributors, or customers, or partners, or other banks, or manufacturers, or semi-autonomous drones, or vehicles in a commercial fleet? You really need to ensure that the trust relationships that you’re encoding into your implementation/deployment truly reflect the legal and IRL [in real life] trust relationships that you have with the entities that are being represented in your system.

And the problem is that, once you’ve deployed that system, it’s likely to be very difficult to backtrack, adjust, or reset the trust relationships that you’ve designed.”

AWS Media Services – Process, Store, and Monetize Cloud-Based Video

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-media-services-process-store-and-monetize-cloud-based-video/

Do you remember what web video was like in the early days? Standalone players, video no larger than a postage stamp, slow & cantankerous connections, overloaded servers, and the ever-present buffering messages were the norm less than two decades ago.

Today, thanks to technological progress and a broad array of standards, things are a lot better. Video consumers are now in control. They use devices of all shapes, sizes, and vintages to enjoy live and recorded content that is broadcast, streamed, or sent over-the-top (OTT, as they say), and expect immediate access to content that captures and then holds their attention. Meeting these expectations presents a challenge for content creators and distributors. Instead of generating video in a one-size-fits-all format, they (or their media servers) must be prepared to produce video that spans a broad range of sizes, formats, and bit rates, taking care to be ready to deal with planned or unplanned surges in demand. In the face of all of this complexity, they must backstop their content with a monetization model that supports the content and the infrastructure to deliver it.

New AWS Media Services
Today we are launching an array of broadcast-quality media services, each designed to address one or more aspects of the challenge that I outlined above. You can use them together to build a complete end-to-end video solution or you can use one or more in building-block style. In true AWS fashion, you can spend more time innovating and less time setting up and running infrastructure, leaving you ready to focus on creating, delivering, and monetizing your content. The services are all elastic, allowing you to ramp up processing power, connections, and storage and giving you the ability to handle million-user (and beyond) spikes with ease.

Here are the services (all accessible from a set of interactive consoles as well as through a comprehensive set of APIs):

AWS Elemental MediaConvert – File-based transcoding for OTT, broadcast, or archiving, with support for a long list of formats and codecs. Features include multi-channel audio, graphic overlays, closed captioning, and several DRM options.

AWS Elemental MediaLive – Live encoding to deliver video streams in real time to both televisions and multiscreen devices. Allows you to deploy highly reliable live channels in minutes, with full control over encoding parameters. It supports ad insertion, multi-channel audio, graphic overlays, and closed captioning.

AWS Elemental MediaPackage – Video origination and just-in-time packaging. Starting from a single input, produces output for multiple devices representing a long list of current and legacy formats. Supports multiple monetization models, time-shifted live streaming, ad insertion, DRM, and blackout management.

AWS Elemental MediaStore – Media-optimized storage that enables high performance and low latency applications such as live streaming, while taking advantage of the scale and durability of Amazon Simple Storage Service (S3).

AWS Elemental MediaTailor – Monetization service that supports ad serving and server-side ad insertion, a broad range of devices, transcoding, and accurate reporting of server-side and client-side ad insertion.

Instead of listing out all of the features in the sections below, I’ve simply included as many screen shots as possible with the expectation that this will give you a better sense of the rich set of features, parameters, and settings that you get with this set of services.

AWS Elemental MediaConvert
MediaConvert allows you to transcode content that is stored in files. You can process individual files or entire media libraries, or anything in-between. You simply create a conversion job that specifies the content and the desired outputs, and submit it to MediaConvert. There’s no software to install or patch and the service scales to meet your needs without affecting turnaround time or performance.

The MediaConvert Console lets you manage Output presets, Job templates, Queues, and Jobs:

You can use a built-in system preset or you can make one of your own. You have full control over the settings when you make your own:

Jobs templates are named, and produce one or more output groups. You can add a new group to a template with a click:

When everything is ready to go, you create a job and make some final selections, then click on Create:

Each account starts with a default queue for jobs, where incoming work is processed in parallel using all processing resources available to the account. Adding queues does not add processing resources, but does cause them to be apportioned across queues. You can temporarily pause one queue in order to devote more resources to the others. You can submit jobs to paused queues and you can also cancel any that have yet to start.

Pricing for this service is based on the amount of video that you process and the features that you use.

AWS Elemental MediaLive
This service is for live encoding, and can be run 24×7. MediaLive channels are deployed on redundant resources distributed in two physically separated Availability Zones in order to provide the reliability expected by our customers in the broadcast industry. You can specify your inputs and define your channels in the MediaLive Console:

After you create an Input, you create a Channel and attach it to the Input:

You have full control over the settings for each channel:

 

AWS Elemental MediaPackage
This service lets you deliver video to many devices from a single source. It focuses on protection and just-in-time packaging, giving you the ability to provide your users with the desired content on the device of their choice. You simply create a channel to get started:

Then you add one or more endpoints. Once again, plenty of options and full control, including a startover window and a time delay:

You find the input URL, user name, and password for your channel and route your live video stream to it for packaging:

AWS Elemental MediaStore
MediaStore offers the performance, consistency, and latency required for live and on-demand media delivery. Objects are written and read into a new “temporal” tier of object storage for a limited amount of time, then move silently into S3 for long-lived durability. You simply create a storage container to group your media content:

The container is available within a minute or so:

Like S3 buckets, MediaStore containers have access policies and no limits on the number of objects or storage capacity.

MediaStore helps you to take full advantage of S3 by managing the object key names so as to maximize storage and retrieval throughput, in accord with the Request Rate and Performance Considerations.

AWS Elemental MediaTailor
This service takes care of server-side ad insertion while providing a broadcast-quality viewer experience by transcoding ad assets on the fly. Your customer’s video player asks MediaTailor for a playlist. MediaTailor, in turn, calls your Ad Decision Server and returns a playlist that references the origin server for your original video and the ads recommended by the Ad Decision Server. The video player makes all of its requests to a single endpoint in order to ensure that client-side ad-blocking is ineffective. You simply create a MediaTailor Configuration:

Context information is passed to the Ad Decision Server in the URL:

Despite the length of this post I have barely scratched the surface of the AWS Media Services. Once AWS re:Invent is in the rear view mirror I hope to do a deep dive and show you how to use each of these services.

Available Now
The entire set of AWS Media Services is available now and you can start using them today! Pricing varies by service, but is built around a pay-as-you-go model.

Jeff;

Event-Driven Computing with Amazon SNS and AWS Compute, Storage, Database, and Networking Services

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/event-driven-computing-with-amazon-sns-compute-storage-database-and-networking-services/

Contributed by Otavio Ferreira, Manager, Software Development, AWS Messaging

Like other developers around the world, you may be tackling increasingly complex business problems. A key success factor, in that case, is the ability to break down a large project scope into smaller, more manageable components. A service-oriented architecture guides you toward designing systems as a collection of loosely coupled, independently scaled, and highly reusable services. Microservices take this even further. To improve performance and scalability, they promote fine-grained interfaces and lightweight protocols.

However, the communication among isolated microservices can be challenging. Services are often deployed onto independent servers and don’t share any compute or storage resources. Also, you should avoid hard dependencies among microservices, to preserve maintainability and reusability.

If you apply the pub/sub design pattern, you can effortlessly decouple and independently scale out your microservices and serverless architectures. A pub/sub messaging service, such as Amazon SNS, promotes event-driven computing that statically decouples event publishers from subscribers, while dynamically allowing for the exchange of messages between them. An event-driven architecture also introduces the responsiveness needed to deal with complex problems, which are often unpredictable and asynchronous.

What is event-driven computing?

Given the context of microservices, event-driven computing is a model in which subscriber services automatically perform work in response to events triggered by publisher services. This paradigm can be applied to automate workflows while decoupling the services that collectively and independently work to fulfil these workflows. Amazon SNS is an event-driven computing hub, in the AWS Cloud, that has native integration with several AWS publisher and subscriber services.

Which AWS services publish events to SNS natively?

Several AWS services have been integrated as SNS publishers and, therefore, can natively trigger event-driven computing for a variety of use cases. In this post, I specifically cover AWS compute, storage, database, and networking services, as depicted below.

Compute services

  • Auto Scaling: Helps you ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application. You can configure Auto Scaling lifecycle hooks to trigger events, as Auto Scaling resizes your EC2 cluster.As an example, you may want to warm up the local cache store on newly launched EC2 instances, and also download log files from other EC2 instances that are about to be terminated. To make this happen, set an SNS topic as your Auto Scaling group’s notification target, then subscribe two Lambda functions to this SNS topic. The first function is responsible for handling scale-out events (to warm up cache upon provisioning), whereas the second is in charge of handling scale-in events (to download logs upon termination).

  • AWS Elastic Beanstalk: An easy-to-use service for deploying and scaling web applications and web services developed in a number of programming languages. You can configure event notifications for your Elastic Beanstalk environment so that notable events can be automatically published to an SNS topic, then pushed to topic subscribers.As an example, you may use this event-driven architecture to coordinate your continuous integration pipeline (such as Jenkins CI). That way, whenever an environment is created, Elastic Beanstalk publishes this event to an SNS topic, which triggers a subscribing Lambda function, which then kicks off a CI job against your newly created Elastic Beanstalk environment.

  • Elastic Load Balancing: Automatically distributes incoming application traffic across Amazon EC2 instances, containers, or other resources identified by IP addresses.You can configure CloudWatch alarms on Elastic Load Balancing metrics, to automate the handling of events derived from Classic Load Balancers. As an example, you may leverage this event-driven design to automate latency profiling in an Amazon ECS cluster behind a Classic Load Balancer. In this example, whenever your ECS cluster breaches your load balancer latency threshold, an event is posted by CloudWatch to an SNS topic, which then triggers a subscribing Lambda function. This function runs a task on your ECS cluster to trigger a latency profiling tool, hosted on the cluster itself. This can enhance your latency troubleshooting exercise by making it timely.

Storage services

  • Amazon S3: Object storage built to store and retrieve any amount of data.You can enable S3 event notifications, and automatically get them posted to SNS topics, to automate a variety of workflows. For instance, imagine that you have an S3 bucket to store incoming resumes from candidates, and a fleet of EC2 instances to encode these resumes from their original format (such as Word or text) into a portable format (such as PDF).In this example, whenever new files are uploaded to your input bucket, S3 publishes these events to an SNS topic, which in turn pushes these messages into subscribing SQS queues. Then, encoding workers running on EC2 instances poll these messages from the SQS queues; retrieve the original files from the input S3 bucket; encode them into PDF; and finally store them in an output S3 bucket.

  • Amazon EFS: Provides simple and scalable file storage, for use with Amazon EC2 instances, in the AWS Cloud.You can configure CloudWatch alarms on EFS metrics, to automate the management of your EFS systems. For example, consider a highly parallelized genomics analysis application that runs against an EFS system. By default, this file system is instantiated on the “General Purpose” performance mode. Although this performance mode allows for lower latency, it might eventually impose a scaling bottleneck. Therefore, you may leverage an event-driven design to handle it automatically.Basically, as soon as the EFS metric “Percent I/O Limit” breaches 95%, CloudWatch could post this event to an SNS topic, which in turn would push this message into a subscribing Lambda function. This function automatically creates a new file system, this time on the “Max I/O” performance mode, then switches the genomics analysis application to this new file system. As a result, your application starts experiencing higher I/O throughput rates.

  • Amazon Glacier: A secure, durable, and low-cost cloud storage service for data archiving and long-term backup.You can set a notification configuration on an Amazon Glacier vault so that when a job completes, a message is published to an SNS topic. Retrieving an archive from Amazon Glacier is a two-step asynchronous operation, in which you first initiate a job, and then download the output after the job completes. Therefore, SNS helps you eliminate polling your Amazon Glacier vault to check whether your job has been completed, or not. As usual, you may subscribe SQS queues, Lambda functions, and HTTP endpoints to your SNS topic, to be notified when your Amazon Glacier job is done.

  • AWS Snowball: A petabyte-scale data transport solution that uses secure appliances to transfer large amounts of data.You can leverage Snowball notifications to automate workflows related to importing data into and exporting data from AWS. More specifically, whenever your Snowball job status changes, Snowball can publish this event to an SNS topic, which in turn can broadcast the event to all its subscribers.As an example, imagine a Geographic Information System (GIS) that distributes high-resolution satellite images to users via Web browser. In this example, the GIS vendor could capture up to 80 TB of satellite images; create a Snowball job to import these files from an on-premises system to an S3 bucket; and provide an SNS topic ARN to be notified upon job status changes in Snowball. After Snowball changes the job status from “Importing” to “Completed”, Snowball publishes this event to the specified SNS topic, which delivers this message to a subscribing Lambda function, which finally creates a CloudFront web distribution for the target S3 bucket, to serve the images to end users.

Database services

  • Amazon RDS: Makes it easy to set up, operate, and scale a relational database in the cloud.RDS leverages SNS to broadcast notifications when RDS events occur. As usual, these notifications can be delivered via any protocol supported by SNS, including SQS queues, Lambda functions, and HTTP endpoints.As an example, imagine that you own a social network website that has experienced organic growth, and needs to scale its compute and database resources on demand. In this case, you could provide an SNS topic to listen to RDS DB instance events. When the “Low Storage” event is published to the topic, SNS pushes this event to a subscribing Lambda function, which in turn leverages the RDS API to increase the storage capacity allocated to your DB instance. The provisioning itself takes place within the specified DB maintenance window.

  • Amazon ElastiCache: A web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud.ElastiCache can publish messages using Amazon SNS when significant events happen on your cache cluster. This feature can be used to refresh the list of servers on client machines connected to individual cache node endpoints of a cache cluster. For instance, an ecommerce website fetches product details from a cache cluster, with the goal of offloading a relational database and speeding up page load times. Ideally, you want to make sure that each web server always has an updated list of cache servers to which to connect.To automate this node discovery process, you can get your ElastiCache cluster to publish events to an SNS topic. Thus, when ElastiCache event “AddCacheNodeComplete” is published, your topic then pushes this event to all subscribing HTTP endpoints that serve your ecommerce website, so that these HTTP servers can update their list of cache nodes.

  • Amazon Redshift: A fully managed data warehouse that makes it simple to analyze data using standard SQL and BI (Business Intelligence) tools.Amazon Redshift uses SNS to broadcast relevant events so that data warehouse workflows can be automated. As an example, imagine a news website that sends clickstream data to a Kinesis Firehose stream, which then loads the data into Amazon Redshift, so that popular news and reading preferences might be surfaced on a BI tool. At some point though, this Amazon Redshift cluster might need to be resized, and the cluster enters a ready-only mode. Hence, this Amazon Redshift event is published to an SNS topic, which delivers this event to a subscribing Lambda function, which finally deletes the corresponding Kinesis Firehose delivery stream, so that clickstream data uploads can be put on hold.At a later point, after Amazon Redshift publishes the event that the maintenance window has been closed, SNS notifies a subscribing Lambda function accordingly, so that this function can re-create the Kinesis Firehose delivery stream, and resume clickstream data uploads to Amazon Redshift.

  • AWS DMS: Helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database.DMS also uses SNS to provide notifications when DMS events occur, which can automate database migration workflows. As an example, you might create data replication tasks to migrate an on-premises MS SQL database, composed of multiple tables, to MySQL. Thus, if replication tasks fail due to incompatible data encoding in the source tables, these events can be published to an SNS topic, which can push these messages into a subscribing SQS queue. Then, encoders running on EC2 can poll these messages from the SQS queue, encode the source tables into a compatible character set, and restart the corresponding replication tasks in DMS. This is an event-driven approach to a self-healing database migration process.

Networking services

  • Amazon Route 53: A highly available and scalable cloud-based DNS (Domain Name System). Route 53 health checks monitor the health and performance of your web applications, web servers, and other resources.You can set CloudWatch alarms and get automated Amazon SNS notifications when the status of your Route 53 health check changes. As an example, imagine an online payment gateway that reports the health of its platform to merchants worldwide, via a status page. This page is hosted on EC2 and fetches platform health data from DynamoDB. In this case, you could configure a CloudWatch alarm for your Route 53 health check, so that when the alarm threshold is breached, and the payment gateway is no longer considered healthy, then CloudWatch publishes this event to an SNS topic, which pushes this message to a subscribing Lambda function, which finally updates the DynamoDB table that populates the status page. This event-driven approach avoids any kind of manual update to the status page visited by merchants.

  • AWS Direct Connect (AWS DX): Makes it easy to establish a dedicated network connection from your premises to AWS, which can reduce your network costs, increase bandwidth throughput, and provide a more consistent network experience than Internet-based connections.You can monitor physical DX connections using CloudWatch alarms, and send SNS messages when alarms change their status. As an example, when a DX connection state shifts to 0 (zero), indicating that the connection is down, this event can be published to an SNS topic, which can fan out this message to impacted servers through HTTP endpoints, so that they might reroute their traffic through a different connection instead. This is an event-driven approach to connectivity resilience.

More event-driven computing on AWS

In addition to SNS, event-driven computing is also addressed by Amazon CloudWatch Events, which delivers a near real-time stream of system events that describe changes in AWS resources. With CloudWatch Events, you can route each event type to one or more targets, including:

Many AWS services publish events to CloudWatch. As an example, you can get CloudWatch Events to capture events on your ETL (Extract, Transform, Load) jobs running on AWS Glue and push failed ones to an SQS queue, so that you can retry them later.

Conclusion

Amazon SNS is a pub/sub messaging service that can be used as an event-driven computing hub to AWS customers worldwide. By capturing events natively triggered by AWS services, such as EC2, S3 and RDS, you can automate and optimize all kinds of workflows, namely scaling, testing, encoding, profiling, broadcasting, discovery, failover, and much more. Business use cases presented in this post ranged from recruiting websites, to scientific research, geographic systems, social networks, retail websites, and news portals.

Start now by visiting Amazon SNS in the AWS Management Console, or by trying the AWS 10-Minute Tutorial, Send Fan-out Event Notifications with Amazon SNS and Amazon SQS.

 

Now Available – Compute-Intensive C5 Instances for Amazon EC2

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-compute-intensive-c5-instances-for-amazon-ec2/

I’m thrilled to announce that the new compute-intensive C5 instances are available today in six sizes for launch in three AWS regions!

These instances designed for compute-heavy applications like batch processing, distributed analytics, high-performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding. The new instances offer a 25% price/performance improvement over the C4 instances, with over 50% for some workloads. They also have additional memory per vCPU, and (for code that can make use of the new AVX-512 instructions), twice the performance for vector and floating point workloads.

Over the years we have been working non-stop to provide our customers with the best possible networking, storage, and compute performance, with a long-term focus on offloading many types of work to dedicated hardware designed and built by AWS. The C5 instance type incorporates the latest generation of our hardware offloads, and also takes another big step forward with the addition of a new hypervisor that runs hand-in-glove with our hardware. The new hypervisor allows us to give you access to all of the processing power provided by the host hardware, while also making performance even more consistent and further raising the bar on security. We’ll be sharing many technical details about it at AWS re:Invent.

The New Instances
The C5 instances are available in six sizes:

Instance Name vCPUs
RAM
EBS Bandwidth Network Bandwidth
c5.large 2 4 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.xlarge 4 8 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.2xlarge 8 16 GiB Up to 2.25 Gbps Up to 10 Gbps
c5.4xlarge 16 32 GiB 2.25 Gbps Up to 10 Gbps
c5.9xlarge 36 72 GiB 4.5 Gbps 10 Gbps
c5.18xlarge 72 144 GiB 9 Gbps 25 Gbps

Each vCPU is a hardware hyperthread on a 3.0 GHz Intel Xeon Platinum 8000-series processor. This custom processor, optimized for EC2, allows you have full control over the C-states on the two largest sizes, allowing you to run a single core at up to 3.5 GHz using Intel Turbo Boost Technology.

As you can see from the table, the four smallest instance sizes offer substantially more EBS and network bandwidth than the previous generation of compute-intensive instances.

Because all networking and storage functionality is implemented in hardware, C5 instances require HVM AMIs that include drivers for the Elastic Network Adapter (ENA) and NVMe. The latest Amazon Linux, Microsoft Windows, Ubuntu, RHEL, CentOS, SLES, Debian, and FreeBSD AMIs all support C5 instances. If you are doing machine learning inferencing, or other compute-intensive work, be sure to check out the most recent version of the Intel Math Kernel Library. It has been optimized for the Intel® Xeon® Platinum processor and has the potential to greatly accelerate your work.

In order to remain compatible with instances that use the Xen hypervisor, the device names for EBS volumes will continue to use the existing /dev/sd and /dev/xvd prefixes. The device name that you provide when you attach a volume to an instance is not used because the NVMe driver assigns its own device name (read Amazon EBS and NVMe to learn more):

The nvme command displays additional information about each volume (install it using sudo yum -y install nvme-cli if necessary):

The SN field in the output can be mapped to an EBS volume ID by inserting a “-” after the “vol” prefix (sadly, the NVMe SN field is not long enough to store the entire ID). Here’s a simple script that uses this information to create an EBS snapshot of each attached volume:

$ sudo nvme list | \
  awk '/dev/ {print(gensub("vol", "vol-", 1, $2))}' | \
  xargs -n 1 aws ec2 create-snapshot --volume-id

With a little more work (and a lot of testing), you could create a script that expands EBS volumes that are getting full.

Getting to C5
As I mentioned earlier, our effort to offload work to hardware accelerators has been underway for quite some time. Here’s a recap:

CC1 – Launched in 2010, the CC1 was designed to support scale-out HPC applications. It was the first EC2 instance to support 10 Gbps networking and one of the first to support HVM virtualization. The network fabric that we designed for the CC1 (based on our own switch hardware) has become the standard for all AWS data centers.

C3 – Launched in 2013, the C3 introduced Enhanced Networking and uses dedicated hardware accelerators to support the software defined network inside of each Virtual Private Cloud (VPC). Hardware virtualization removes the I/O stack from the hypervisor in favor of direct access by the guest OS, resulting in higher performance and reduced variability.

C4 – Launched in 2015, the C4 instances are EBS Optimized by default via a dedicated network connection, and also offload EBS processing (including CPU-intensive crypto operations for encrypted EBS volumes) to a hardware accelerator.

C5 – Launched today, the hypervisor that powers the C5 instances allow practically all of the resources of the host CPU to be devoted to customer instances. The ENA networking and the NVMe interface to EBS are both powered by hardware accelerators. The instances do not require (or support) the Xen paravirtual networking or block device drivers, both of which have been removed in order to increase efficiency.

Going forward, we’ll use this hypervisor to power other instance types and plan to share additional technical details in a set of AWS re:Invent sessions.

Launch a C5 Today
You can launch C5 instances today in the US East (Northern Virginia), US West (Oregon), and EU (Ireland) Regions in On-Demand and Spot form (Reserved Instances are also available), with additional Regions in the works.

One quick note before I go: The current NVMe driver is not optimized for high-performance sequential workloads and we don’t recommend the use of C5 instances in conjunction with sc1 or st1 volumes. We are aware of this issue and have been working to optimize the driver for this important use case.

Jeff;

Using Enhanced Request Authorizers in Amazon API Gateway

Post Syndicated from Stefano Buliani original https://aws.amazon.com/blogs/compute/using-enhanced-request-authorizers-in-amazon-api-gateway/

Recently, AWS introduced a new type of authorizer in Amazon API Gateway, enhanced request authorizers. Previously, custom authorizers received only the bearer token included in the request and the ARN of the API Gateway method being called. Enhanced request authorizers receive all of the headers, query string, and path parameters as well as the request context. This enables you to make more sophisticated authorization decisions based on parameters such as the client IP address, user agent, or a query string parameter alongside the client bearer token.

Enhanced request authorizer configuration

From the API Gateway console, you can declare a new enhanced request authorizer by selecting the Request option as the AWS Lambda event payload:

Create enhanced request authorizer

 

Just like normal custom authorizers, API Gateway can cache the policy returned by your Lambda function. With enhanced request authorizers, however, you can also specify the values that form the unique key of a policy in the cache. For example, if your authorization decision is based on both the bearer token and the IP address of the client, both values should be part of the unique key in the policy cache. The identity source parameter lets you specify these values as mapping expressions:

  • The bearer token appears in the Authorization header
  • The client IP address is stored in the sourceIp parameter of the request context.

Configure identity sources

 

Using enhanced request authorizers with Swagger

You can also define enhanced request authorizers in your Swagger (Open API) definitions. In the following example, you can see that all of the options configured in the API Gateway console are available as custom extensions in the API definition. For example, the identitySource field is a comma-separated list of mapping expressions.

securityDefinitions:
  IpAuthorizer:
    type: "apiKey"
    name: "IpAuthorizer"
    in: "header"
    x-amazon-apigateway-authtype: "custom"
    x-amazon-apigateway-authorizer:
      authorizerResultTtlInSeconds: 300
      identitySource: "method.request.header.Authorization, context.identity.sourceIp"
      authorizerUri: "arn:aws:apigateway:us-east-1:lambda:path/2015-03-31/functions/arn:aws:lambda:us-east-1:XXXXXXXXXX:function:py-ip-authorizer/invocations"
      type: "request"

After you have declared your authorizer in the security definitions section, you can use it in your API methods:

---
swagger: "2.0"
info:
  title: "request-authorizer-demo"
basePath: "/dev"
paths:
  /hello:
    get:
      security:
      - IpAuthorizer: []
...

Enhanced request authorizer Lambda functions

Enhanced request authorizer Lambda functions receive an event object that is similar to proxy integrations. It contains all of the information about a request, excluding the body.

{
    "methodArn": "arn:aws:execute-api:us-east-1:XXXXXXXXXX:xxxxxx/dev/GET/hello",
    "resource": "/hello",
    "requestContext": {
        "resourceId": "xxxx",
        "apiId": "xxxxxxxxx",
        "resourcePath": "/hello",
        "httpMethod": "GET",
        "requestId": "9e04ff18-98a6-11e7-9311-ef19ba18fc8a",
        "path": "/dev/hello",
        "accountId": "XXXXXXXXXXX",
        "identity": {
            "apiKey": "",
            "sourceIp": "58.240.196.186"
        },
        "stage": "dev"
    },
    "queryStringParameters": {},
    "httpMethod": "GET",
    "pathParameters": {},
    "headers": {
        "cache-control": "no-cache",
        "x-amzn-ssl-client-hello": "AQACJAMDAAAAAAAAAAAAAAAAAAAAAAAAAAAA…",
        "Accept-Encoding": "gzip, deflate",
        "X-Forwarded-For": "54.240.196.186, 54.182.214.90",
        "Accept": "*/*",
        "User-Agent": "PostmanRuntime/6.2.5",
        "Authorization": "hello"
    },
    "stageVariables": {},
    "path": "/hello",
    "type": "REQUEST"
}

The following enhanced request authorizer snippet is written in Python and compares the source IP address against a list of valid IP addresses. The comments in the code explain what happens in each step.

...
VALID_IPS = ["58.240.195.186", "201.246.162.38"]

def lambda_handler(event, context):

    # Read the client’s bearer token.
    jwtToken = event["headers"]["Authorization"]
    
    # Read the source IP address for the request form 
    # for the API Gateway context object.
    clientIp = event["requestContext"]["identity"]["sourceIp"]
    
    # Verify that the client IP address is allowed.
    # If it’s not valid, raise an exception to make sure
    # that API Gateway returns a 401 status code.
    if clientIp not in VALID_IPS:
        raise Exception('Unauthorized')
    
    # Only allow hello users in!
    if not validate_jwt(userId):
        raise Exception('Unauthorized')

    # Use the values from the event object to populate the 
    # required parameters in the policy object.
    policy = AuthPolicy(userId, event["requestContext"]["accountId"])
    policy.restApiId = event["requestContext"]["apiId"]
    policy.region = event["methodArn"].split(":")[3]
    policy.stage = event["requestContext"]["stage"]
    
    # Use the scopes from the bearer token to make a 
    # decision on which methods to allow in the API.
    policy.allowMethod(HttpVerb.GET, '/hello')

    # Finally, build the policy.
    authResponse = policy.build()

    return authResponse
...

Conclusion

API Gateway customers build complex APIs, and authorization decisions often go beyond the simple properties in a JWT token. For example, users may be allowed to call the “list cars” endpoint but only with a specific subset of filter parameters. With enhanced request authorizers, you have access to all request parameters. You can centralize all of your application’s access control decisions in a Lambda function, making it easier to manage your application security.

SecureLogin For Java Web Applications

Post Syndicated from Bozho original https://techblog.bozho.net/securelogin-java-web-applications/

No, there is not a missing whitespace in the title. It’s not about any secure login, it’s about the SecureLogin protocol developed by Egor Homakov, a security consultant, who became famous for committing to master in the Rails project without having permissions.

The SecureLogin protocol is very interesting, as it does not rely on any central party (e.g. OAuth providers like Facebook and Twitter), thus avoiding all the pitfalls of OAuth (which Homakov has often criticized). It is not a password manager either. It is just a client-side software that performs a bit of crypto in order to prove to the server that it is indeed the right user. For that to work, two parts are key:

  • Using a master password to generate a private key. It uses a key-derivation function, which guarantees that the produced private key has sufficient entropy. That way, using the same master password and the same email, you will get the same private key everytime you use the password, and therefore the same public key. And you are the only one who can prove this public key is yours, by signing a message with your private key.
  • Service providers (websites) identify you by your public key by storing it in the database when you register and then looking it up on each subsequent login

The client-side part is performed ideally by a native client – a browser plugin (one is available for Chrome) or a OS-specific application (including mobile ones). That may sound tedious, but it’s actually quick and easy and a one-time event (and is easier than password managers).

I have to admit – I like it, because I’ve been having a similar idea for a while. In my “biometric identification” presentation (where I discuss the pitfalls of using biometrics-only identification schemes), I proposed (slide 23) an identification scheme that uses biometrics (e.g. scanned with your phone) + a password to produce a private key (using a key-derivation function). And the biometric can easily be added to SecureLogin in the future.

It’s not all roses, of course, as one issue isn’t fully resolved yet – revocation. In case someone steals your master password (or you suspect it might be stolen), you may want to change it and notify all service providers of that change so that they can replace your old public key with a new one. That has two implications – first, you may not have a full list of sites that you registered on, and since you may have changed devices, or used multiple devices, there may be websites that never get to know about your password change. There are proposed solutions (points 3 and 4), but they are not intrinsic to the protocol and rely on centralized services. The second issue is – what if the attacker changes your password first? To prevent that, service providers should probably rely on email verification, which is neither part of the protocol, nor is encouraged by it. But you may have to do it anyway, as a safeguard.

Homakov has not only defined a protocol, but also provided implementations of the native clients, so that anyone can start using it. So I decided to add it to a project I’m currently working on (the login page is here). For that I needed a java implementation of the server verification, and since no such implementation existed (only ruby and node.js are provided for now), I implemented it myself. So if you are going to use SecureLogin with a Java web application, you can use that instead of rolling out your own. While implementing it, I hit a few minor issues that may lead to protocol changes, so I guess backward compatibility should also be somehow included in the protocol (through versioning).

So, how does the code look like? On the client side you have a button and a little javascript:

<!-- get the latest sdk.js from the GitHub repo of securelogin
   or include it from https://securelogin.pw/sdk.js -->
<script src="js/securelogin/sdk.js"></script>
....
<p class="slbutton" id="securelogin">&#9889; SecureLogin</p>
$("#securelogin").click(function() {
  SecureLogin(function(sltoken){
	// TODO: consider adding csrf protection as in the demo applications
        // Note - pass as request body, not as param, as the token relies 
        // on url-encoding which some frameworks mess with
	$.post('/app/user/securelogin', sltoken, function(result) {
            if(result == 'ok') {
		 window.location = "/app/";
            } else {
                 $.notify("Login failed, try again later", "error");
            }
	});
  });
  return false;
});

A single button can be used for both login and signup, or you can have a separate signup form, if it has to include additional details rather than just an email. Since I added SecureLogin in addition to my password-based login, I kept the two forms.

On the server, you simply do the following:

@RequestMapping(value = "/securelogin/register", method = RequestMethod.POST)
@ResponseBody
public String secureloginRegister(@RequestBody String token, HttpServletResponse response) {
    try {
        SecureLogin login = SecureLogin.verify(request.getSecureLoginToken(), Options.create(websiteRootUrl));
        UserDetails details = userService.getUserDetailsByEmail(login.getEmail());
        if (details == null || !login.getRawPublicKey().equals(details.getSecureLoginPublicKey())) {
            return "failure";
        }
        // sets the proper cookies to the response
        TokenAuthenticationService.addAuthentication(response, login.getEmail(), secure));
        return "ok";
    } catch (SecureLoginVerificationException e) {
        return "failure";
    }
}

This is spring-mvc, but it can be any web framework. You can also incorporate that into a spring-security flow somehow. I’ve never liked spring-security’s complexity, so I did it manually. Also, instead of strings, you can return proper status codes. Note that I’m doing a lookup by email and only then checking the public key (as if it’s a password). You can do the other way around if you have the proper index on the public key column.

I wouldn’t suggest having a SecureLogin-only system, as the project is still in an early stage and users may not be comfortable with it. But certainly adding it as an option is a good idea.

The post SecureLogin For Java Web Applications appeared first on Bozho's tech blog.

Backblaze’s Upgrade Guide for macOS High Sierra

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/macos-high-sierra-upgrade-guide/

High Sierra

Apple introduced macOS 10.13 “High Sierra” at its 2017 Worldwide Developers Conference in June. On Tuesday, we learned we don’t have long to wait — the new OS will be available on September 25. It’s a free upgrade, and millions of Mac users around the world will rush to install it.

We understand. A new OS from Apple is exciting, But please, before you upgrade, we want to remind you to back up your Mac. You want your data to be safe from unexpected problems that could happen in the upgrade. We do, too. To make that easier, Backblaze offers this macOS High Sierra upgrade guide.

Why Upgrade to macOS 10.13 High Sierra?

High Sierra, as the name suggests, is a follow-on to the previous macOS, Sierra. Its major focus is on improving the base OS with significant improvements that will support new capabilities in the future in the file system, video, graphics, and virtual/augmented reality.

But don’t despair; there also are outward improvements that will be readily apparent to everyone when they boot the OS for the first time. We’ll cover both the inner and outer improvements coming in this new OS.

Under the Hood of High Sierra

APFS (Apple File System)

Apple has been rolling out its first file system upgrade for a while now. It’s already in iOS: now High Sierra brings APFS to the Mac. Apple touts APFS as a new file system optimized for Flash/SSD storage and featuring strong encryption, better and faster file handling, safer copying and moving of files, and other improved file system fundamentals.

We went into detail about the enhancements and improvements that APFS has over the previous file system, HFS+, in an earlier post. Many of these improvements, including enhanced performance, security and reliability of data, will provide immediate benefits to users, while others provide a foundation for future storage innovations and will require work by Apple and third parties to support in their products and services.

Most of us won’t notice these improvements, but we’ll benefit from better, faster, and safer file handling, which I think all of us can appreciate.

Video

High Sierra includes High Efficiency Video Encoding (HEVC, aka H.265), which preserves better detail and color while also introducing improved compression over H.264 (MPEG-4 AVC). Even existing Macs will benefit from the HEVC software encoding in High Sierra, but newer Mac models include HEVC hardware acceleration for even better performance.

MacBook Pro

Metal 2

macOS High Sierra introduces Metal 2, the next-generation of Apple’s Metal graphics API that was launched three years ago. Apple claims that Metal 2 provides up to 10x better performance in key areas. It provides near-direct access to the graphics processor (GPU), enabling the GPU to take control over key aspects of the rendering pipeline. Metal 2 will enhance the Mac’s capability for machine learning, and is the technology driving the new virtual reality platform on Macs.

audio video editor screenshot

Virtual Reality

We’re about to see an explosion of virtual reality experiences on both the Mac and iOS thanks to High Sierra and iOS 11. Content creators will be able to use apps like Final Cut Pro X, Epic Unreal 4 Editor, and Unity Editor to create fully immersive worlds that will revolutionize entertainment and education and have many professional uses, as well.

Users will want the new iMac with Retina 5K display or the upcoming iMac Pro to enjoy them, or any supported Mac paired with the latest external GPU and VR headset.

iMac and HTC virtual reality player

Outward Improvements

Siri

Siri logo

Expect a more nature voice from Siri in High Sierra. She or he will be less robotic, with greater expression and use of intonation in speech. Siri will also learn more about your preferences in things like music, helping you choose music that fits your taste and putting together playlists expressly for you. Expect Siri to be able to answer your questions about music-related trivia, as well.

Siri:  what does “scaramouche” refer to in the song Bohemian Rhapsody?

Photos

HD MacBook Pro screenshot

Photos has been redesigned with a new layout and new tools. A redesigned Edit view includes new tools for fine-tuning color and contrast and making adjustments within a defined color range. Some fun elements for creating special effects and memories also have been added. Photos now works with external apps such as Photoshop and Pixelmator. Compatibility with third-party extension adds printing and publishing services to help get your photos out into the world.

Safari

Safari logo

Apple claims that Safari in High Sierra is the world’s fastest desktop browser, outperforming Chrome and other browsers in a range of benchmark tests. They’ve also added autoplay blocking for those pesky videos that play without your permission and tracking blocking to help protect your privacy.

Can My Mac Run macOS High Sierra 10.13?

All Macs introduced in mid 2010 or later are compatible. MacBook and iMac computers introduced in late 2009 are also compatible. You’ll need OS X 10.7.5 “Lion” or later installed, along with at least 2 GB RAM and 8.8 GB of available storage to manage the upgrade.
Some features of High Sierra require an internet connection or an Apple ID. You can check to see if your Mac is compatible with High Sierra on Apple’s website.

Conquering High Sierra — What Do I Do Before I Upgrade?

Back Up That Mac!

It’s always smart to back up before you upgrade the operating system or make any other crucial changes to your computer. Upgrading your OS is a major change to your computer, and if anything goes wrong…well, you don’t want that to happen.

iMac backup screenshot

We recommend the 3-2-1 Backup Strategy to make sure your data is safe. What does that mean? Have three copies of your data. There’s the “live” version on your Mac, a local backup (Time Machine, another copy on a local drive or other computer), and an offsite backup like Backblaze. No matter what happens to your computer, you’ll have a way to restore the files if anything goes wrong. Need help understanding how to back up your Mac? We have you covered with a handy Mac backup guide.

Check for App and Driver Updates

This is when it helps to do your homework. Check with app developers or device manufacturers to find if their apps and devices have updates to work with High Sierra. Visit their websites or use the Check for Updates feature built into most apps (often found in the File or Help menus).

If you’ve downloaded apps through the Mac App Store, make sure to open them and click on the Updates button to download the latest updates.

Updating can be hit or miss when you’ve installed apps that didn’t come from the Mac App Store. To make it easier, visit the MacUpdate website. MacUpdate tracks changes to thousands of Mac apps.


Will Backblaze work with macOS High Sierra?

Yes. We’ve taken care to ensure that Backblaze works with High Sierra. We’ve already enhanced our Macintosh client to report the space available on an APFS container and we plan to add additional support for APFS capabilities that enhance Backblaze’s capabilities in the future.

Of course, we’ll watch Apple’s release carefully for any last minute surprises. We’ll officially offer support for High Sierra once we’ve had a chance to thoroughly test the release version.


Set Aside Time for the Upgrade

Depending on the speed of your Internet connection and your computer, upgrading to High Sierra will take some time. You’ll be able to use your Mac straightaway after answering a few questions at the end of the upgrade process.

If you’re going to install High Sierra on multiple Macs, a time-and-bandwidth-saving tip came from a Backblaze customer who suggested copying the installer from your Mac’s Applications folder to a USB Flash drive (or an external drive) before you run it. The installer routinely deletes itself once the upgrade process is completed, but if you grab it before that happens you can use it on other computers.

Where Do I get High Sierra?

Apple says that High Sierra will be available on September 25. Like other Mac operating system releases, Apple offers macOS 10.13 High Sierra for download from the Mac App Store, which is included on the Mac. As long as your Mac is supported and running OS X 10.7.5 “Lion” (released in 2012) or later, you can download and run the installer. It’s free. Thank you, Apple.

Better to be Safe than Sorry

Back up your Mac before doing anything to it, and make Backblaze part of your 3-2-1 backup strategy. That way your data is secure. Even if you have to roll back after an upgrade, or if you run into other problems, your data will be safe and sound in your backup.

Tell us How it Went

Are you getting ready to install High Sierra? Still have questions? Let us know in the comments. Tell us how your update went and what you like about the new release of macOS.

And While You’re Waiting for High Sierra…

While you’re waiting for Apple to release High Sierra on September 25, you might want to check out these other posts about using your Mac and Backblaze.

The post Backblaze’s Upgrade Guide for macOS High Sierra appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Disabling Intel Hyper-Threading Technology on Amazon EC2 Windows Instances

Post Syndicated from Brian Beach original https://aws.amazon.com/blogs/compute/disabling-intel-hyper-threading-technology-on-amazon-ec2-windows-instances/

In a prior post, Disabling Intel Hyper-Threading on Amazon Linux, I investigated how the Linux kernel enumerates CPUs. I also discussed the options to disable Intel Hyper-Threading (HT Technology) in Amazon Linux running on Amazon EC2.

In this post, I do the same for Microsoft Windows Server 2016 running on EC2 instances. I begin with a quick review of HT Technology and the reasons you might want to disable it. I also recommend that you take a moment to review the prior post for a more thorough foundation.

HT Technology

HT Technology makes a single physical processor appear as multiple logical processors. Each core in an Intel Xeon processor has two threads of execution. Most of the time, these threads can progress independently; one thread executing while the other is waiting on a relatively slow operation (for example, reading from memory) to occur. However, the two threads do share resources and occasionally one thread is forced to wait while the other is executing.

There a few unique situations where disabling HT Technology can improve performance. One example is high performance computing (HPC) workloads that rely heavily on floating point operations. In these rare cases, it can be advantageous to disable HT Technology. However, these cases are rare, and for the overwhelming majority of workloads you should leave it enabled. I recommend that you test with and without HT Technology enabled, and only disable threads if you are sure it will improve performance.

Exploring HT Technology on Microsoft Windows

Here’s how Microsoft Windows enumerates CPUs. As before, I am running these examples on an m4.2xlarge. I also chose to run Windows Server 2016, but you can walk through these exercises on any version of Windows. Remember that the m4.2xlarge has eight vCPUs, and each vCPU is a thread of an Intel Xeon core. Therefore, the m4.2xlarge has four cores, each of which run two threads, resulting in eight vCPUs.

Windows does not have a built-in utility to examine CPU configuration, but you can download the Sysinternals coreinfo utility from Microsoft’s website. This utility provides useful information about the system CPU and memory topology. For this walkthrough, you enumerate the individual CPUs, which you can do by running coreinfo -c. For example:

C:\Users\Administrator >coreinfo -c

Coreinfo v3.31 - Dump information on system CPU and memory topology
Copyright (C) 2008-2014 Mark Russinovich
Sysinternals - www.sysinternals.com

Logical to Physical Processor Map:
**------ Physical Processor 0 (Hyperthreaded)
--**---- Physical Processor 1 (Hyperthreaded)
----**-- Physical Processor 2 (Hyperthreaded)
------** Physical Processor 3 (Hyperthreaded)

As you can see from the screenshot, the coreinfo utility displays a table where each row is a physical core and each column is a logical CPU. In other words, the two asterisks on the first line indicate that CPU 0 and CPU 1 are the two threads in the first physical core. Therefore, my m4.2xlarge has for four physical processors and each processor has two threads resulting in eight total CPUs, just as expected.

It is interesting to note that Windows Server 2016 enumerates CPUs in a different order than Linux. Remember from the prior post that Linux enumerated the first thread in each core, followed by the second thread in each core. You can see from the output earlier that Windows Server 2016, enumerates both threads in the first core, then both threads in the second core, and so on. The diagram below shows the relationship of CPUs to cores and threads in both operating systems.

In the Linux post, I disabled CPUs 4–6, leaving one thread per core, and effectively disabling HT Technology. You can see from the diagram that you must disable the odd-numbered threads (that is, 1, 3, 5, and 7) to achieve the same result in Windows. Here’s how to do that.

Disabling HT Technology on Microsoft Windows

In Linux, you can globally disable CPUs dynamically. In Windows, there is no direct equivalent that I could find, but there are a few alternatives.

First, you can disable CPUs using the msconfig.exe tool. If you choose Boot, Advanced Options, you have the option to set the number of processors. In the example below, I limit my m4.2xlarge to four CPUs. Restart for this change to take effect.

Unfortunately, Windows does not disable hyperthreaded CPUs first and then real cores, as Linux does. As you can see in the following output, coreinfo reports that my c4.2xlarge has two real cores and four hyperthreads, after rebooting. Msconfig.exe is useful for disabling cores, but it does not allow you to disable HT Technology.

Note: If you have been following along, you can re-enable all your CPUs by unselecting the Number of processors check box and rebooting your system.

 

C:\Users\Administrator >coreinfo -c

Coreinfo v3.31 - Dump information on system CPU and memory topology
Copyright (C) 2008-2014 Mark Russinovich
Sysinternals - www.sysinternals.com

Logical to Physical Processor Map:
**-- Physical Processor 0 (Hyperthreaded)
--** Physical Processor 1 (Hyperthreaded)

While you cannot disable HT Technology systemwide, Windows does allow you to associate a particular process with one or more CPUs. Microsoft calls this, “processor affinity”. To see an example, use the following steps.

  1. Launch an instance of Notepad.
  2. Open Windows Task Manager and choose Processes.
  3. Open the context (right click) menu on notepad.exe and choose Set Affinity….

This brings up the Processor Affinity dialog box.

As you can see, all the CPUs are allowed to run this instance of notepad.exe. You can uncheck a few CPUs to exclude them. Windows is smart enough to allow any scheduled operations to continue to completion on disabled CPUs. It then saves its state at the next scheduling event, and resumes those operations on another CPU. To ensure that only one thread in each core is able to run a process, you uncheck every other core. This effectively disables HT Technology for this process. For example:

Of course, this can be tedious when you have a large number of cores. Remember that the x1.32xlarge has 128 CPUs. Luckily, you can set the affinity of a running process from PowerShell using the Get-Process cmdlet. For example:

PS C:\&gt; (Get-Process -Name 'notepad').ProcessorAffinity = 0x55;

The ProcessorAffinity attribute takes a bitmask in hexadecimal format. 0x55 in hex is equivalent to 01010101 in binary. Think of the binary encoding as 1=enabled and 0=disabled. This is slightly confusing, but we work left to right so that CPU 0 is the rightmost bit and CPU 7 is the leftmost bit. Therefore, 01010101 means that the first thread in each CPU is enabled just as it was in the diagram earlier.

The calculator built into Windows includes a “programmer view” that helps you convert from hexadecimal to binary. In addition, the ProcessorAffinity attribute is a 64-bit number. Therefore, you can only configure the processor affinity on systems up to 64 CPUs. At the moment, only the x1.32xlarge has more than 64 vCPUs.

In the preceding examples, you changed the processor affinity of a running process. Sometimes, you want to start a process with the affinity already configured. You can do this using the start command. The start command includes an affinity flag that takes a hexadecimal number like the PowerShell example earlier.

C:\Users\Administrator&gt;start /affinity 55 notepad.exe

It is interesting to note that a child process inherits the affinity from its parent. For example, the following commands create a batch file that launches Notepad, and starts the batch file with the affinity set. If you examine the instance of Notepad launched by the batch file, you see that the affinity has been applied to as well.

C:\Users\Administrator&gt;echo notepad.exe > test.bat
C:\Users\Administrator&gt;start /affinity 55 test.bat

This means that you can set the affinity of your task scheduler and any tasks that the scheduler starts inherits the affinity. So, you can disable every other thread when you launch the scheduler and effectively disable HT Technology for all of the tasks as well. Be sure to test this point, however, as some schedulers override the normal inheritance behavior and explicitly set processor affinity when starting a child process.

Conclusion

While the Windows operating system does not allow you to disable logical CPUs, you can set processor affinity on individual processes. You also learned that Windows Server 2016 enumerates CPUs in a different order than Linux. Therefore, you can effectively disable HT Technology by restricting a process to every other CPU. Finally, you learned how to set affinity of both new and running processes using Task Manager, PowerShell, and the start command.

Note: this technical approach has nothing to do with control over software licensing, or licensing rights, which are sometimes linked to the number of “CPUs” or “cores.” For licensing purposes, those are legal terms, not technical terms. This post did not cover anything about software licensing or licensing rights.

If you have questions or suggestions, please comment below.

From Data Lake to Data Warehouse: Enhancing Customer 360 with Amazon Redshift Spectrum

Post Syndicated from Dylan Tong original https://aws.amazon.com/blogs/big-data/from-data-lake-to-data-warehouse-enhancing-customer-360-with-amazon-redshift-spectrum/

Achieving a 360o-view of your customer has become increasingly challenging as companies embrace omni-channel strategies, engaging customers across websites, mobile, call centers, social media, physical sites, and beyond. The promise of a web where online and physical worlds blend makes understanding your customers more challenging, but also more important. Businesses that are successful in this medium have a significant competitive advantage.

The big data challenge requires the management of data at high velocity and volume. Many customers have identified Amazon S3 as a great data lake solution that removes the complexities of managing a highly durable, fault tolerant data lake infrastructure at scale and economically.

AWS data services substantially lessen the heavy lifting of adopting technologies, allowing you to spend more time on what matters most—gaining a better understanding of customers to elevate your business. In this post, I show how a recent Amazon Redshift innovation, Redshift Spectrum, can enhance a customer 360 initiative.

Customer 360 solution

A successful customer 360 view benefits from using a variety of technologies to deliver different forms of insights. These could range from real-time analysis of streaming data from wearable devices and mobile interactions to historical analysis that requires interactive, on demand queries on billions of transactions. In some cases, insights can only be inferred through AI via deep learning. Finally, the value of your customer data and insights can’t be fully realized until it is operationalized at scale—readily accessible by fleets of applications. Companies are leveraging AWS for the breadth of services that cover these domains, to drive their data strategy.

A number of AWS customers stream data from various sources into a S3 data lake through Amazon Kinesis. They use Kinesis and technologies in the Hadoop ecosystem like Spark running on Amazon EMR to enrich this data. High-value data is loaded into an Amazon Redshift data warehouse, which allows users to analyze and interact with data through a choice of client tools. Redshift Spectrum expands on this analytics platform by enabling Amazon Redshift to blend and analyze data beyond the data warehouse and across a data lake.

The following diagram illustrates the workflow for such a solution.

This solution delivers value by:

  • Reducing complexity and time to value to deeper insights. For instance, an existing data model in Amazon Redshift may provide insights across dimensions such as customer, geography, time, and product on metrics from sales and financial systems. Down the road, you may gain access to streaming data sources like customer-care call logs and website activity that you want to blend in with the sales data on the same dimensions to understand how web and call center experiences maybe correlated with sales performance. Redshift Spectrum can join these dimensions in Amazon Redshift with data in S3 to allow you to quickly gain new insights, and avoid the slow and more expensive alternative of fully integrating these sources with your data warehouse.
  • Providing an additional avenue for optimizing costs and performance. In cases like call logs and clickstream data where volumes could be many TBs to PBs, storing the data exclusively in S3 yields significant cost savings. Interactive analysis on massive datasets may now be economically viable in cases where data was previously analyzed periodically through static reports generated by inexpensive batch processes. In some cases, you can improve the user experience while simultaneously lowering costs. Spectrum is powered by a large-scale infrastructure external to your Amazon Redshift cluster, and excels at scanning and aggregating large volumes of data. For instance, your analysts maybe performing data discovery on customer interactions across millions of consumers over years of data across various channels. On this large dataset, certain queries could be slow if you didn’t have a large Amazon Redshift cluster. Alternatively, you could use Redshift Spectrum to achieve a better user experience with a smaller cluster.

Proof of concept walkthrough

To make evaluation easier for you, I’ve conducted a Redshift Spectrum proof-of-concept (PoC) for the customer 360 use case. For those who want to replicate the PoC, the instructions, AWS CloudFormation templates, and public data sets are available in the GitHub repository.

The remainder of this post is a journey through the project, observing best practices in action, and learning how you can achieve business value. The walkthrough involves:

  • An analysis of performance data from the PoC environment involving queries that demonstrate blending and analysis of data across Amazon Redshift and S3. Observe that great results are achievable at scale.
  • Guidance by example on query tuning, design, and data preparation to illustrate the optimization process. This includes tuning a query that combines clickstream data in S3 with customer and time dimensions in Amazon Redshift, and aggregates ~1.9 B out of 3.7 B+ records in under 10 seconds with a small cluster!
  • Guidance and measurements to help assess deciding between two options: accessing and analyzing data exclusively in Amazon Redshift, or using Redshift Spectrum to access data left in S3.

Stream ingestion and enrichment

The focus of this post isn’t stream ingestion and enrichment on Kinesis and EMR, but be mindful of performance best practices on S3 to ensure good streaming and query performance:

  • Use random object keys: The data files provided for this project are prefixed with SHA-256 hashes to prevent hot partitions. This is important to ensure that optimal request rates to support PUT requests from the incoming stream in addition to certain queries from large Amazon Redshift clusters that could send a large number of parallel GET requests.
  • Micro-batch your data stream: S3 isn’t optimized for small random write workloads. Your datasets should be micro-batched into large files. For instance, the “parquet-1” dataset provided batches >7 million records per file. The optimal file size for Redshift Spectrum is usually in the 100 MB to 1 GB range.

If you have an edge case that may pose scalability challenges, AWS would love to hear about it. For further guidance, talk to your solutions architect.

Environment

The project consists of the following environment:

  • Amazon Redshift cluster: 4 X dc1.large
  • Data:
    • Time and customer dimension tables are stored on all Amazon Redshift nodes (ALL distribution style):
      • The data originates from the DWDATE and CUSTOMER tables in the Star Schema Benchmark
      • The customer table contains attributes for 3 million customers.
      • The time data is at the day-level granularity, and spans 7 years, from the start of 1992 to the end of 1998.
    • The clickstream data is stored in an S3 bucket, and serves as a fact table.
      • Various copies of this dataset in CSV and Parquet format have been provided, for reasons to be discussed later.
      • The data is a modified version of the uservisits dataset from AMPLab’s Big Data Benchmark, which was generated by Intel’s Hadoop benchmark tools.
      • Changes were minimal, so that existing test harnesses for this test can be adapted:
        • Increased the 751,754,869-row dataset 5X to 3,758,774,345 rows.
        • Added surrogate keys to support joins with customer and time dimensions. These keys were distributed evenly across the entire dataset to represents user visits from six customers over seven years.
        • Values for the visitDate column were replaced to align with the 7-year timeframe, and the added time surrogate key.

Queries across the data lake and data warehouse 

Imagine a scenario where a business analyst plans to analyze clickstream metrics like ad revenue over time and by customer, market segment and more. The example below is a query that achieves this effect: 

The query part highlighted in red retrieves clickstream data in S3, and joins the data with the time and customer dimension tables in Amazon Redshift through the part highlighted in blue. The query returns the total ad revenue for three customers over the last three months, along with info on their respective market segment.

Unfortunately, this query takes around three minutes to run, and doesn’t enable the interactive experience that you want. However, there’s a number of performance optimizations that you can implement to achieve the desired performance.

Performance analysis

Two key utilities provide visibility into Redshift Spectrum:

  • EXPLAIN
    Provides the query execution plan, which includes info around what processing is pushed down to Redshift Spectrum. Steps in the plan that include the prefix S3 are executed on Redshift Spectrum. For instance, the plan for the previous query has the step “S3 Seq Scan clickstream.uservisits_csv10”, indicating that Redshift Spectrum performs a scan on S3 as part of the query execution.
  • SVL_S3QUERY_SUMMARY
    Statistics for Redshift Spectrum queries are stored in this table. While the execution plan presents cost estimates, this table stores actual statistics for past query runs.

You can get the statistics of your last query by inspecting the SVL_S3QUERY_SUMMARY table with the condition (query = pg_last_query_id()). Inspecting the previous query reveals that the entire dataset of nearly 3.8 billion rows was scanned to retrieve less than 66.3 million rows. Improving scan selectivity in your query could yield substantial performance improvements.

Partitioning

Partitioning is a key means to improving scan efficiency. In your environment, the data and tables have already been organized, and configured to support partitions. For more information, see the PoC project setup instructions. The clickstream table was defined as:

CREATE EXTERNAL TABLE clickstream.uservisits_csv10
…
PARTITIONED BY(customer int4, visitYearMonth int4)

The entire 3.8 billion-row dataset is organized as a collection of large files where each file contains data exclusive to a particular customer and month in a year. This allows you to partition your data into logical subsets by customer and year/month. With partitions, the query engine can target a subset of files:

  • Only for specific customers
  • Only data for specific months
  • A combination of specific customers and year/months

You can use partitions in your queries. Instead of joining your customer data on the surrogate customer key (that is, c.c_custkey = uv.custKey), the partition key “customer” should be used instead:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, SUM(uv.adRevenue)
…
ON c.c_custkey = uv.customer
…
ORDER BY c.c_name, c.c_mktsegment, uv.yearMonthKey  ASC

This query should run approximately twice as fast as the previous query. If you look at the statistics for this query in SVL_S3QUERY_SUMMARY, you see that only half the dataset was scanned. This is expected because your query is on three out of six customers on an evenly distributed dataset. However, the scan is still inefficient, and you can benefit from using your year/month partition key as well:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, SUM(uv.adRevenue)
…
ON c.c_custkey = uv.customer
…
ON uv.visitYearMonth = t.d_yearmonthnum
…
ORDER BY c.c_name, c.c_mktsegment, uv.visitYearMonth ASC

All joins between the tables are now using partitions. Upon reviewing the statistics for this query, you should observe that Redshift Spectrum scans and returns the exact number of rows, 66,270,117. If you run this query a few times, you should see execution time in the range of 8 seconds, which is a 22.5X improvement on your original query!

Predicate pushdown and storage optimizations 

Previously, I mentioned that Redshift Spectrum performs processing through large-scale infrastructure external to your Amazon Redshift cluster. It is optimized for performing large scans and aggregations on S3. In fact, Redshift Spectrum may even out-perform a medium size Amazon Redshift cluster on these types of workloads with the proper optimizations. There are two important variables to consider for optimizing large scans and aggregations:

  • File size and count. As a general rule, use files 100 MB-1 GB in size, as Redshift Spectrum and S3 are optimized for reading this object size. However, the number of files operating on a query is directly correlated with the parallelism achievable by a query. There is an inverse relationship between file size and count: the bigger the files, the fewer files there are for the same dataset. Consequently, there is a trade-off between optimizing for object read performance, and the amount of parallelism achievable on a particular query. Large files are best for large scans as the query likely operates on sufficiently large number of files. For queries that are more selective and for which fewer files are operating, you may find that smaller files allow for more parallelism.
  • Data format. Redshift Spectrum supports various data formats. Columnar formats like Parquet can sometimes lead to substantial performance benefits by providing compression and more efficient I/O for certain workloads. Generally, format types like Parquet should be used for query workloads involving large scans, and high attribute selectivity. Again, there are trade-offs as formats like Parquet require more compute power to process than plaintext. For queries on smaller subsets of data, the I/O efficiency benefit of Parquet is diminished. At some point, Parquet may perform the same or slower than plaintext. Latency, compression rates, and the trade-off between user experience and cost should drive your decision.

To help illustrate how Redshift Spectrum performs on these large aggregation workloads, run a basic query that aggregates the entire ~3.7 billion record dataset on Redshift Spectrum, and compared that with running the query exclusively on Amazon Redshift:

SELECT uv.custKey, COUNT(uv.custKey)
FROM <your clickstream table> as uv
GROUP BY uv.custKey
ORDER BY uv.custKey ASC

For the Amazon Redshift test case, the clickstream data is loaded, and distributed evenly across all nodes (even distribution style) with optimal column compression encodings prescribed by the Amazon Redshift’s ANALYZE command.

The Redshift Spectrum test case uses a Parquet data format with each file containing all the data for a particular customer in a month. This results in files mostly in the range of 220-280 MB, and in effect, is the largest file size for this partitioning scheme. If you run tests with the other datasets provided, you see that this data format and size is optimal and out-performs others by ~60X. 

Performance differences will vary depending on the scenario. The important takeaway is to understand the testing strategy and the workload characteristics where Redshift Spectrum is likely to yield performance benefits. 

The following chart compares the query execution time for the two scenarios. The results indicate that you would have to pay for 12 X DC1.Large nodes to get performance comparable to using a small Amazon Redshift cluster that leverages Redshift Spectrum. 

Chart showing simple aggregation on ~3.7 billion records

So you’ve validated that Spectrum excels at performing large aggregations. Could you benefit by pushing more work down to Redshift Spectrum in your original query? It turns out that you can, by making the following modification:

The clickstream data is stored at a day-level granularity for each customer while your query rolls up the data to the month level per customer. In the earlier query that uses the day/month partition key, you optimized the query so that it only scans and retrieves the data required, but the day level data is still sent back to your Amazon Redshift cluster for joining and aggregation. The query shown here pushes aggregation work down to Redshift Spectrum as indicated by the query plan:

In this query, Redshift Spectrum aggregates the clickstream data to the month level before it is returned to the Amazon Redshift cluster and joined with the dimension tables. This query should complete in about 4 seconds, which is roughly twice as fast as only using the partition key. The speed increase is evident upon reviewing the SVL_S3QUERY_SUMMARY table:

  • Bytes scanned is 21.6X less because of the Parquet data format.
  • Only 90 records are returned back to the Amazon Redshift cluster as a result of the push-down, instead of ~66.2 million, leading to substantially less join overhead, and about 530 MB less data sent back to your cluster.
  • No adverse change in average parallelism.

Assessing the value of Amazon Redshift vs. Redshift Spectrum

At this point, you might be asking yourself, why would I ever not use Redshift Spectrum? Well, you still get additional value for your money by loading data into Amazon Redshift, and querying in Amazon Redshift vs. querying S3.

In fact, it turns out that the last version of our query runs even faster when executed exclusively in native Amazon Redshift, as shown in the following chart:

Chart comparing Amazon Redshift vs. Redshift Spectrum with pushdown aggregation over 3 months of data

As a general rule, queries that aren’t dominated by I/O and which involve multiple joins are better optimized in native Amazon Redshift. For instance, the performance difference between running the partition key query entirely in Amazon Redshift versus with Redshift Spectrum is twice as large as that that of the pushdown aggregation query, partly because the former case benefits more from better join performance.

Furthermore, the variability in latency in native Amazon Redshift is lower. For use cases where you have tight performance SLAs on queries, you may want to consider using Amazon Redshift exclusively to support those queries.

On the other hand, when you perform large scans, you could benefit from the best of both worlds: higher performance at lower cost. For instance, imagine that you wanted to enable your business analysts to interactively discover insights across a vast amount of historical data. In the example below, the pushdown aggregation query is modified to analyze seven years of data instead of three months:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, uv.totalRevenue
…
WHERE customer <= 3 and visitYearMonth >= 199201
… 
FROM dwdate WHERE d_yearmonthnum >= 199201) as t
…
ORDER BY c.c_name, c.c_mktsegment, uv.visitYearMonth ASC

This query requires scanning and aggregating nearly 1.9 billion records. As shown in the chart below, Redshift Spectrum substantially speeds up this query. A large Amazon Redshift cluster would have to be provisioned to support this use case. With the aid of Redshift Spectrum, you could use an existing small cluster, keep a single copy of your data in S3, and benefit from economical, durable storage while only paying for what you use via the pay per query pricing model.

Chart comparing Amazon Redshift vs. Redshift Spectrum with pushdown aggregation over 7 years of data

Summary

Redshift Spectrum lowers the time to value for deeper insights on customer data queries spanning the data lake and data warehouse. It can enable interactive analysis on datasets in cases that weren’t economically practical or technically feasible before.

There are cases where you can get the best of both worlds from Redshift Spectrum: higher performance at lower cost. However, there are still latency-sensitive use cases where you may want native Amazon Redshift performance. For more best practice tips, see the 10 Best Practices for Amazon Redshift post.

Please visit the Amazon Redshift Spectrum PoC Environment Github page. If you have questions or suggestions, please comment below.

 


Additional Reading

Learn more about how Amazon Redshift Spectrum extends data warehousing out to exabytes – no loading required.


About the Author

Dylan Tong is an Enterprise Solutions Architect at AWS. He works with customers to help drive their success on the AWS platform through thought leadership and guidance on designing well architected solutions. He has spent most of his career building on his expertise in data management and analytics by working for leaders and innovators in the space.

 

 

Hacking a Gene Sequencer by Encoding Malware in a DNA Strand

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/08/hacking_a_gene_.html

One of the common ways to hack a computer is to mess with its input data. That is, if you can feed the computer data that it interprets — or misinterprets — in a particular way, you can trick the computer into doing things that it wasn’t intended to do. This is basically what a buffer overflow attack is: the data input overflows a buffer and ends up being executed by the computer process.

Well, some researchers did this with a computer that processes DNA, and they encoded their malware in the DNA strands themselves:

To make the malware, the team translated a simple computer command into a short stretch of 176 DNA letters, denoted as A, G, C, and T. After ordering copies of the DNA from a vendor for $89, they fed the strands to a sequencing machine, which read off the gene letters, storing them as binary digits, 0s and 1s.

Erlich says the attack took advantage of a spill-over effect, when data that exceeds a storage buffer can be interpreted as a computer command. In this case, the command contacted a server controlled by Kohno’s team, from which they took control of a computer in their lab they were using to analyze the DNA file.

News articles. Research paper.