Tag Archives: Mac

masscan, macOS, and firewall

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/masscan-macos-and-firewall.html

One of the more useful features of masscan is the “–banners” check, which connects to the TCP port, sends some request, and gets a basic response back. However, since masscan has it’s own TCP stack, it’ll interfere with the operating system’s TCP stack if they are sharing the same IPv4 address. The operating system will reply with a RST packet before the TCP connection can be established.

The way to fix this is to use the built-in packet-filtering firewall to block those packets in the operating-system TCP/IP stack. The masscan program still sees everything before the packet-filter, but the operating system can’t see anything after the packet-filter.

Note that we are talking about the “packet-filter” firewall feature here. Remember that macOS, like most operating systems these days, has two separate firewalls: an application firewall and a packet-filter firewall. The application firewall is the one you see in System Settings labeled “Firewall”, and it controls things based upon the application’s identity rather than by which ports it uses. This is normally “on” by default. The packet-filter is normally “off” by default and is of little use to normal users.

Also note that macOS changed packet-filters around version 10.10.5 (“Yosemite”, October 2014). The older one is known as “ipfw“, which was the default firewall for FreeBSD (much of macOS is based on FreeBSD). The replacement is known as PF, which comes from OpenBSD. Whereas you used to use the old “ipfw” command on the command line, you now use the “pfctl” command, as well as the “/etc/pf.conf” configuration file.

What we need to filter is the source port of the packets that masscan will send, so that when replies are received, they won’t reach the operating-system stack, and just go to masscan instead. To do this, we need find a range of ports that won’t conflict with the operating system. Namely, when the operating system creates outgoing connections, it randomly chooses a source port within a certain range. We want to use masscan to use source ports in a different range.

To figure out the range macOS uses, we run the following command:

sysctl net.inet.ip.portrange.first net.inet.ip.portrange.last

On my laptop, which is probably the default for macOS, I get the following range. Sniffing with Wireshark confirms this is the range used for source ports for outgoing connections.

net.inet.ip.portrange.first: 49152
net.inet.ip.portrange.last: 65535

So this means I shouldn’t use source ports anywhere in the range 49152 to 65535. On my laptop, I’ve decided to use for masscan the ports 40000 to 41023. The range masscan uses must be a power of 2, so here I’m using 1024 (two to the tenth power).

To configure masscan, I can either type the parameter “–source-port 40000-41023” every time I run the program, or I can add the following line to /etc/masscan/masscan.conf. Remember that by default, masscan will look in that configuration file for any configuration parameters, so you don’t have to keep retyping them on the command line.

source-port = 40000-41023

Next, I need to add the following firewall rule to the bottom of /etc/pf.conf:

block in proto tcp from any to any port 40000 >< 41024

However, we aren’t done yet. By default, the packet-filter firewall is off. Therefore, every time you reboot your computer, you need to enable it. The simple way to do this is on the command line run:

pfctl -e

Or, if that doesn’t work, try:

pfctl -E

Ideally, you’d want it to start automatically on bootup. I haven’t figure out how to do this one macOS in an approved fashion that doesn’t conflict with something else. Apparently there are a few GUIs that will do this for you.

AWS IoT 1-Click – Use Simple Devices to Trigger Lambda Functions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-iot-1-click-use-simple-devices-to-trigger-lambda-functions/

We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.

I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:

Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.

Camera Control – Tim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.

Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!

Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.

All About AWS IoT 1-Click
As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:

Architects can dream up applications for inexpensive, low-powered devices.

Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.

Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.

Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.

I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.

Who’s Got the Button?
We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.

The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.

The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.

We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at [email protected].

AWS IoT 1-Click Concepts
I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.

Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.

Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.

Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).

Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”

Getting Started with AWS IoT 1-Click
Let’s set up an IoT button using the AWS IoT 1-Click Console:

If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):

The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:

Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:

I am now ready to put them to use. I click on Projects, and then Create a project:

I name and describe my project, and click Next to proceed:

Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):

The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:

I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:

I can inspect my project and see that everything looks good:

I click on the buttons and the SMS messages appear:

I can monitor device activity in the AWS IoT 1-Click Console:

And also in the Lambda Console:

The Lambda function itself is also accessible, and can be used as-is or customized:

As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.

Now Available
I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!

Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.

To learn more, visit the AWS IoT 1-Click home page or read the AWS IoT 1-Click documentation.

— Jeff;

 

Some notes on eFail

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/some-notes-on-efail.html

I’ve been busy trying to replicate the “eFail” PGP/SMIME bug. I thought I’d write up some notes.

PGP and S/MIME encrypt emails, so that eavesdroppers can’t read them. The bugs potentially allow eavesdroppers to take the encrypted emails they’ve captured and resend them to you, reformatted in a way that allows them to decrypt the messages.

Disable remote/external content in email

The most important defense is to disable “external” or “remote” content from being automatically loaded. This is when HTML-formatted emails attempt to load images from remote websites. This happens legitimately when they want to display images, but not fill up the email with them. But most of the time this is illegitimate, they hide images on the webpage in order to track you with unique IDs and cookies. For example, this is the code at the end of an email from politician Bernie Sanders to his supporters. Notice the long random number assigned to track me, and the width/height of this image is set to one pixel, so you don’t even see it:

Such trackers are so pernicious they are disabled by default in most email clients. This is an example of the settings in Thunderbird:

The problem is that as you read email messages, you often get frustrated by the fact the error messages and missing content, so you keep adding exceptions:

The correct defense against this eFail bug is to make sure such remote content is disabled and that you have no exceptions, or at least, no HTTP exceptions. HTTPS exceptions (those using SSL) are okay as long as they aren’t to a website the attacker controls. Unencrypted exceptions, though, the hacker can eavesdrop on, so it doesn’t matter if they control the website the requests go to. If the attacker can eavesdrop on your emails, they can probably eavesdrop on your HTTP sessions as well.

Some have recommended disabling PGP and S/MIME completely. That’s probably overkill. As long as the attacker can’t use the “remote content” in emails, you are fine. Likewise, some have recommend disabling HTML completely. That’s not even an option in any email client I’ve used — you can disable sending HTML emails, but not receiving them. It’s sufficient to just disable grabbing remote content, not the rest of HTML email rendering.

I couldn’t replicate the direct exfiltration

There rare two related bugs. One allows direct exfiltration, which appends the decrypted PGP email onto the end of an IMG tag (like one of those tracking tags), allowing the entire message to be decrypted.

An example of this is the following email. This is a standard HTML email message consisting of multiple parts. The trick is that the IMG tag in the first part starts the URL (blog.robertgraham.com/…) but doesn’t end it. It has the starting quotes in front of the URL but no ending quotes. The ending will in the next chunk.

The next chunk isn’t HTML, though, it’s PGP. The PGP extension (in my case, Enignmail) will detect this and automatically decrypt it. In this case, it’s some previous email message I’ve received the attacker captured by eavesdropping, who then pastes the contents into this email message in order to get it decrypted.

What should happen at this point is that Thunderbird will generate a request (if “remote content” is enabled) to the blog.robertgraham.com server with the decrypted contents of the PGP email appended to it. But that’s not what happens. Instead, I get this:

I am indeed getting weird stuff in the URL (the bit after the GET /), but it’s not the PGP decrypted message. Instead what’s going on is that when Thunderbird puts together a “multipart/mixed” message, it adds it’s own HTML tags consisting of lines between each part. In the email client it looks like this:

The HTML code it adds looks like:

That’s what you see in the above URL, all this code up to the first quotes. Those quotes terminate the quotes in the URL from the first multipart section, causing the rest of the content to be ignored (as far as being sent as part of the URL).

So at least for the latest version of Thunderbird, you are accidentally safe, even if you have “remote content” enabled. Though, this is only according to my tests, there may be a work around to this that hackers could exploit.

STARTTLS

In the old days, email was sent plaintext over the wire so that it could be passively eavesdropped on. Nowadays, most providers send it via “STARTTLS”, which sorta encrypts it. Attackers can still intercept such email, but they have to do so actively, using man-in-the-middle. Such active techniques can be detected if you are careful and look for them.
Some organizations don’t care. Apparently, some nation states are just blocking all STARTTLS and forcing email to be sent unencrypted. Others do care. The NSA will passively sniff all the email they can in nations like Iraq, but they won’t actively intercept STARTTLS messages, for fear of getting caught.
The consequence is that it’s much less likely that somebody has been eavesdropping on you, passively grabbing all your PGP/SMIME emails. If you fear they have been, you should look (e.g. send emails from GMail and see if they are intercepted by sniffing the wire).

You’ll know if you are getting hacked

If somebody attacks you using eFail, you’ll know. You’ll get an email message formatted this way, with multipart/mixed components, some with corrupt HTML, some encrypted via PGP. This means that for the most part, your risk is that you’ll be attacked only once — the hacker will only be able to get one message through and decrypt it before you notice that something is amiss. Though to be fair, they can probably include all the emails they want decrypted as attachments to the single email they sent you, so the risk isn’t necessarily that you’ll only get one decrypted.
As mentioned above, a lot of attackers (e.g. the NSA) won’t attack you if its so easy to get caught. Other attackers, though, like anonymous hackers, don’t care.
Somebody ought to write a plugin to Thunderbird to detect this.

Summary

It only works if attackers have already captured your emails (though, that’s why you use PGP/SMIME in the first place, to guard against that).
It only works if you’ve enabled your email client to automatically grab external/remote content.
It seems to not be easily reproducible in all cases.
Instead of disabling PGP/SMIME, you should make sure your email client hast remote/external content disabled — that’s a huge privacy violation even without this bug.
Notes: The default email client on the Mac enables remote content by default, which is bad:

A serverless solution for invoking AWS Lambda at a sub-minute frequency

Post Syndicated from Emanuele Menga original https://aws.amazon.com/blogs/architecture/a-serverless-solution-for-invoking-aws-lambda-at-a-sub-minute-frequency/

If you’ve used Amazon CloudWatch Events to schedule the invocation of a Lambda function at regular intervals, you may have noticed that the highest frequency possible is one invocation per minute. However, in some cases, you may need to invoke Lambda more often than that. In this blog post, I’ll cover invoking a Lambda function every 10 seconds, but with some simple math you can change to whatever interval you like.

To achieve this, I’ll show you how to leverage Step Functions and Amazon Kinesis Data Streams.

The Solution

For this example, I’ve created a Step Functions State Machine that invokes our Lambda function 6 times, 10 seconds apart. Such State Machine is then executed once per minute by a CloudWatch Events Rule. This state machine is then executed once per minute by an Amazon CloudWatch Events rule. Finally, the Kinesis Data Stream triggers our Lambda function for each record inserted. The result is our Lambda function being invoked every 10 seconds, indefinitely.

Below is a diagram illustrating how the various services work together.

Step 1: My sampleLambda function doesn’t actually do anything, it just simulates an execution for a few seconds. This is the (Python) code of my dummy function:

import time

import random


def lambda_handler(event, context):

rand = random.randint(1, 3)

print('Running for {} seconds'.format(rand))

time.sleep(rand)

return True

Step 2:

The next step is to create a second Lambda function, that I called Iterator, which has two duties:

  • It keeps track of the current number of iterations, since Step Function doesn’t natively have a state we can use for this purpose.
  • It asynchronously invokes our Lambda function at every loops.

This is the code of the Iterator, adapted from here.

 

import boto3

client = boto3.client('kinesis')

def lambda_handler(event, context):

index = event['iterator']['index'] + 1

response = client.put_record(

StreamName='LambdaSubMinute',

PartitionKey='1',

Data='',

)

return {

'index': index,

'continue': index < event['iterator']['count'],

'count': event['iterator']['count']

}

This function does three things:

  • Increments the counter.
  • Verifies if we reached a count of (in this example) 6.
  • Sends an empty record to the Kinesis Stream.

Now we can create the Step Functions State Machine; the definition is, again, adapted from here.

 

{

"Comment": "Invoke Lambda every 10 seconds",

"StartAt": "ConfigureCount",

"States": {

"ConfigureCount": {

"Type": "Pass",

"Result": {

"index": 0,

"count": 6

},

"ResultPath": "$.iterator",

"Next": "Iterator"

},

"Iterator": {

"Type": "Task",

"Resource": “arn:aws:lambda:REGION:ACCOUNT_ID:function:Iterator",

"ResultPath": "$.iterator",

"Next": "IsCountReached"

},

"IsCountReached": {

"Type": "Choice",

"Choices": [

{

"Variable": "$.iterator.continue",

"BooleanEquals": true,

"Next": "Wait"

}

],

"Default": "Done"

},

"Wait": {

"Type": "Wait",

"Seconds": 10,

"Next": "Iterator"

},

"Done": {

"Type": "Pass",

"End": true

}

}

}

This is how it works:

  1. The state machine starts and sets the index at 0 and the count at 6.
  2. Iterator function is invoked.
  3. If the iterator function reached the end of the loop, the IsCountReached state terminates the execution, otherwise the machine waits for 10 seconds.
  4. The machine loops back to the iterator.

Step 3: Create an Amazon CloudWatch Events rule scheduled to trigger every minute and add the state machine as its target. I’ve actually prepared an Amazon CloudFormation template that creates the whole stack and starts the Lambda invocations, you can find it here.

Performance

Let’s have a look at a sample series of invocations and analyse how precise the timing is. In the following chart I reported the delay (in excess of the expected 10-second-wait) of 30 consecutive invocations of my dummy function, when the Iterator is configured with a memory size of 1024MB.

Invocations Delay

Notice the delay increases by a few hundred milliseconds at every invocation. The good news is it accrues only within the same loop, 6 times; after that, a new CloudWatch Events kicks in and it resets.

This delay  is due to the work that AWS Step Function does outside of the Wait state, the main component of which is the Iterator function itself, that runs synchronously in the state machine and therefore adds up its duration to the 10-second-wait.

As we can easily imagine, the memory size of the Iterator Lambda function does make a difference. Here are the Average and Maximum duration of the function with 256MB, 512MB, 1GB and 2GB of memory.

Average Duration

Maximum Duration


Given those results, I’d say that a memory of 1024MB is a good compromise between costs and performance.

Caveats

As mentioned, in our Amazon CloudWatch Events documentation, in rare cases a rule can be triggered twice, causing two parallel executions of the state machine. If that is a concern, we can add a task state at the beginning of the state machine that checks if any other executions are currently running. If the outcome is positive, then a choice state can immediately terminate the flow. Since the state machine is invoked every 60 seconds and runs for about 50, it is safe to assume that executions should all be sequential and any parallel executions should be treated as duplicates. The task state that checks for current running executions can be a Lambda function similar to the following:

 

import boto3

client = boto3.client('stepfunctions')

def lambda_handler(event, context):

response = client.list_executions(

stateMachineArn='arn:aws:states:REGION:ACCOUNTID:stateMachine:LambdaSubMinute',

statusFilter='RUNNING'

)

return {

'alreadyRunning': len(response['executions']) > 0

}

About the Author

Emanuele Menga, Cloud Support Engineer

 

Securing Your Cryptocurrency

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/backing-up-your-cryptocurrency/

Securing Your Cryptocurrency

In our blog post on Tuesday, Cryptocurrency Security Challenges, we wrote about the two primary challenges faced by anyone interested in safely and profitably participating in the cryptocurrency economy: 1) make sure you’re dealing with reputable and ethical companies and services, and, 2) keep your cryptocurrency holdings safe and secure.

In this post, we’re going to focus on how to make sure you don’t lose any of your cryptocurrency holdings through accident, theft, or carelessness. You do that by backing up the keys needed to sell or trade your currencies.

$34 Billion in Lost Value

Of the 16.4 million bitcoins said to be in circulation in the middle of 2017, close to 3.8 million may have been lost because their owners no longer are able to claim their holdings. Based on today’s valuation, that could total as much as $34 billion dollars in lost value. And that’s just bitcoins. There are now over 1,500 different cryptocurrencies, and we don’t know how many of those have been misplaced or lost.



Now that some cryptocurrencies have reached (at least for now) staggering heights in value, it’s likely that owners will be more careful in keeping track of the keys needed to use their cryptocurrencies. For the ones already lost, however, the owners have been separated from their currencies just as surely as if they had thrown Benjamin Franklins and Grover Clevelands over the railing of a ship.

The Basics of Securing Your Cryptocurrencies

In our previous post, we reviewed how cryptocurrency keys work, and the common ways owners can keep track of them. A cryptocurrency owner needs two keys to use their currencies: a public key that can be shared with others is used to receive currency, and a private key that must be kept secure is used to spend or trade currency.

Many wallets and applications allow the user to require extra security to access them, such as a password, or iris, face, or thumb print scan. If one of these options is available in your wallets, take advantage of it. Beyond that, it’s essential to back up your wallet, either using the backup feature built into some applications and wallets, or manually backing up the data used by the wallet. When backing up, it’s a good idea to back up the entire wallet, as some wallets require additional private data to operate that might not be apparent.

No matter which backup method you use, it is important to back up often and have multiple backups, preferable in different locations. As with any valuable data, a 3-2-1 backup strategy is good to follow, which ensures that you’ll have a good backup copy if anything goes wrong with one or more copies of your data.

One more caveat, don’t reuse passwords. This applies to all of your accounts, but is especially important for something as critical as your finances. Don’t ever use the same password for more than one account. If security is breached on one of your accounts, someone could connect your name or ID with other accounts, and will attempt to use the password there, as well. Consider using a password manager such as LastPass or 1Password, which make creating and using complex and unique passwords easy no matter where you’re trying to sign in.

Approaches to Backing Up Your Cryptocurrency Keys

There are numerous ways to be sure your keys are backed up. Let’s take them one by one.

1. Automatic backups using a backup program

If you’re using a wallet program on your computer, for example, Bitcoin Core, it will store your keys, along with other information, in a file. For Bitcoin Core, that file is wallet.dat. Other currencies will use the same or a different file name and some give you the option to select a name for the wallet file.

To back up the wallet.dat or other wallet file, you might need to tell your backup program to explicitly back up that file. Users of Backblaze Backup don’t have to worry about configuring this, since by default, Backblaze Backup will back up all data files. You should determine where your particular cryptocurrency, wallet, or application stores your keys, and make sure the necessary file(s) are backed up if your backup program requires you to select which files are included in the backup.

Backblaze B2 is an option for those interested in low-cost and high security cloud storage of their cryptocurrency keys. Backblaze B2 supports 2-factor verification for account access, works with a number of apps that support automatic backups with encryption, error-recovery, and versioning, and offers an API and command-line interface (CLI), as well. The first 10GB of storage is free, which could be all one needs to store encrypted cryptocurrency keys.

2. Backing up by exporting keys to a file

Apps and wallets will let you export your keys from your app or wallet to a file. Once exported, your keys can be stored on a local drive, USB thumb drive, DAS, NAS, or in the cloud with any cloud storage or sync service you wish. Encrypting the file is strongly encouraged — more on that later. If you use 1Password or LastPass, or other secure notes program, you also could store your keys there.

3. Backing up by saving a mnemonic recovery seed

A mnemonic phrase, mnemonic recovery phrase, or mnemonic seed is a list of words that stores all the information needed to recover a cryptocurrency wallet. Many wallets will have the option to generate a mnemonic backup phrase, which can be written down on paper. If the user’s computer no longer works or their hard drive becomes corrupted, they can download the same wallet software again and use the mnemonic recovery phrase to restore their keys.

The phrase can be used by anyone to recover the keys, so it must be kept safe. Mnemonic phrases are an excellent way of backing up and storing cryptocurrency and so they are used by almost all wallets.

A mnemonic recovery seed is represented by a group of easy to remember words. For example:

eye female unfair moon genius pipe nuclear width dizzy forum cricket know expire purse laptop scale identify cube pause crucial day cigar noise receive

The above words represent the following seed:

0a5b25e1dab6039d22cd57469744499863962daba9d2844243fec 9c0313c1448d1a0b2cd9e230a78775556f9b514a8be45802c2808e fd449a20234e9262dfa69

These words have certain properties:

  • The first four letters are enough to unambiguously identify the word.
  • Similar words are avoided (such as: build and built).

Bitcoin and most other cryptocurrencies such as Litecoin, Ethereum, and others use mnemonic seeds that are 12 to 24 words long. Other currencies might use different length seeds.

4. Physical backups — Paper, Metal

Some cryptocurrency holders believe that their backup, or even all their cryptocurrency account information, should be stored entirely separately from the internet to avoid any risk of their information being compromised through hacks, exploits, or leaks. This type of storage is called “cold storage.” One method of cold storage involves printing out the keys to a piece of paper and then erasing any record of the keys from all computer systems. The keys can be entered into a program from the paper when needed, or scanned from a QR code printed on the paper.

Printed public and private keys

Printed public and private keys

Some who go to extremes suggest separating the mnemonic needed to access an account into individual pieces of paper and storing those pieces in different locations in the home or office, or even different geographical locations. Some say this is a bad idea since it could be possible to reconstruct the mnemonic from one or more pieces. How diligent you wish to be in protecting these codes is up to you.

Mnemonic recovery phrase booklet

Mnemonic recovery phrase booklet

There’s another option that could make you the envy of your friends. That’s the CryptoSteel wallet, which is a stainless steel metal case that comes with more than 250 stainless steel letter tiles engraved on each side. Codes and passwords are assembled manually from the supplied part-randomized set of tiles. Users are able to store up to 96 characters worth of confidential information. Cryptosteel claims to be fireproof, waterproof, and shock-proof.

image of a Cryptosteel cold storage device

Cryptosteel cold wallet

Of course, if you leave your Cryptosteel wallet in the pocket of a pair of ripped jeans that gets thrown out by the housekeeper, as happened to the character Russ Hanneman on the TV show Silicon Valley in last Sunday’s episode, then you’re out of luck. That fictional billionaire investor lost a USB drive with $300 million in cryptocoins. Let’s hope that doesn’t happen to you.

Encryption & Security

Whether you store your keys on your computer, an external disk, a USB drive, DAS, NAS, or in the cloud, you want to make sure that no one else can use those keys. The best way to handle that is to encrypt the backup.

With Backblaze Backup for Windows and Macintosh, your backups are encrypted in transmission to the cloud and on the backup server. Users have the option to add an additional level of security by adding a Personal Encryption Key (PEK), which secures their private key. Your cryptocurrency backup files are secure in the cloud. Using our web or mobile interface, previous versions of files can be accessed, as well.

Our object storage cloud offering, Backblaze B2, can be used with a variety of applications for Windows, Macintosh, and Linux. With B2, cryptocurrency users can choose whichever method of encryption they wish to use on their local computers and then upload their encrypted currency keys to the cloud. Depending on the client used, versioning and life-cycle rules can be applied to the stored files.

Other backup programs and systems provide some or all of these capabilities, as well. If you are backing up to a local drive, it is a good idea to encrypt the local backup, which is an option in some backup programs.

Address Security

Some experts recommend using a different address for each cryptocurrency transaction. Since the address is not the same as your wallet, this means that you are not creating a new wallet, but simply using a new identifier for people sending you cryptocurrency. Creating a new address is usually as easy as clicking a button in the wallet.

One of the chief advantages of using a different address for each transaction is anonymity. Each time you use an address, you put more information into the public ledger (blockchain) about where the currency came from or where it went. That means that over time, using the same address repeatedly could mean that someone could map your relationships, transactions, and incoming funds. The more you use that address, the more information someone can learn about you. For more on this topic, refer to Address reuse.

Note that a downside of using a paper wallet with a single key pair (type-0 non-deterministic wallet) is that it has the vulnerabilities listed above. Each transaction using that paper wallet will add to the public record of transactions associated with that address. Newer wallets, i.e. “deterministic” or those using mnemonic code words support multiple addresses and are now recommended.

There are other approaches to keeping your cryptocurrency transaction secure. Here are a couple of them.

Multi-signature

Multi-signature refers to requiring more than one key to authorize a transaction, much like requiring more than one key to open a safe. It is generally used to divide up responsibility for possession of cryptocurrency. Standard transactions could be called “single-signature transactions” because transfers require only one signature — from the owner of the private key associated with the currency address (public key). Some wallets and apps can be configured to require more than one signature, which means that a group of people, businesses, or other entities all must agree to trade in the cryptocurrencies.

Deep Cold Storage

Deep cold storage ensures the entire transaction process happens in an offline environment. There are typically three elements to deep cold storage.

First, the wallet and private key are generated offline, and the signing of transactions happens on a system not connected to the internet in any manner. This ensures it’s never exposed to a potentially compromised system or connection.

Second, details are secured with encryption to ensure that even if the wallet file ends up in the wrong hands, the information is protected.

Third, storage of the encrypted wallet file or paper wallet is generally at a location or facility that has restricted access, such as a safety deposit box at a bank.

Deep cold storage is used to safeguard a large individual cryptocurrency portfolio held for the long term, or for trustees holding cryptocurrency on behalf of others, and is possibly the safest method to ensure a crypto investment remains secure.

Keep Your Software Up to Date

You should always make sure that you are using the latest version of your app or wallet software, which includes important stability and security fixes. Installing updates for all other software on your computer or mobile device is also important to keep your wallet environment safer.

One Last Thing: Think About Your Testament

Your cryptocurrency funds can be lost forever if you don’t have a backup plan for your peers and family. If the location of your wallets or your passwords is not known by anyone when you are gone, there is no hope that your funds will ever be recovered. Taking a bit of time on these matters can make a huge difference.

To the Moon*

Are you comfortable with how you’re managing and backing up your cryptocurrency wallets and keys? Do you have a suggestion for keeping your cryptocurrencies safe that we missed above? Please let us know in the comments.

*To the Moon — Crypto slang for a currency that reaches an optimistic price projection.

The post Securing Your Cryptocurrency appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Introducing the AWS Machine Learning Competency for Consulting Partners

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/introducing-the-aws-machine-learning-competency-for-consulting-partners/

Today I’m excited to announce a new Machine Learning Competency for Consulting Partners in the Amazon Partner Network (APN). This AWS Competency program allows APN Consulting Partners to demonstrate a deep expertise in machine learning on AWS by providing solutions that enable machine learning and data science workflows for their customers. This new AWS Competency is in addition to the Machine Learning comptency for our APN Technology Partners, that we launched at the re:Invent 2017 partner summit.

These APN Consulting Partners help organizations solve their machine learning and data challenges through:

  • Providing data services that help data scientists and machine learning practitioners prepare their enterprise data for training.
  • Platform solutions that provide data scientists and machine learning practitioners with tools to take their data, train models, and make predictions on new data.
  • SaaS and API solutions to enable predictive capabilities within customer applications.

Why work with an AWS Machine Learning Competency Partner?

The AWS Competency Program helps customers find the most qualified partners with deep expertise. AWS Machine Learning Competency Partners undergo a strict validation of their capabilities to demonstrate technical proficiency and proven customer success with AWS machine learning tools.

If you’re an AWS customer interested in machine learning workloads on AWS, check out our AWS Machine Learning launch partners below:

 

Interested in becoming an AWS Machine Learning Competency Partner?

APN Partners with experience in Machine Learning can learn more about becoming an AWS Machine Learning Competency Partner here. To learn more about the benefits of joining the AWS Partner Network, see our APN Partner website.

Thanks to the AWS Partner Team for their help with this post!
– Randall

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT – Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PT – Data Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PT – Data Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PT – Accelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PT – How to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT – 5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT – .NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PT – Hybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PT – Using AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PT – Using Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PT – Gaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.

Mobile

May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

Networking

May 31, 2018 | 09:00 AM – 09:45 AM PT – Making Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.

Serverless

May 22, 2018 | 01:00 PM – 01:45 PM PT – Building API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.

Storage

May 30, 2018 | 11:00 AM – 11:45 AM PT – Accelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PT – Learn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.

 

 

 

 

Video Deters People From Pirate Sites…Or Encourages Them to Start One?

Post Syndicated from Andy original https://torrentfreak.com/video-deters-people-from-pirate-sites-or-encourages-them-to-start-one-180505/

There are almost as many anti-piracy strategies as there are techniques for downloading.

Litigation and education are probably the two most likely to be seen by the public, who are often directly targeted by the entertainment industries.

Over the years this has led to many campaigns, one of which famously stated that piracy is a crime while equating it to the physical theft of a car, a handbag, a television, or a regular movie DVD. It’s debatable whether these campaigns have made much difference but they have raised awareness and some of the responses have been hilarious.

While success remains hard to measure, it hasn’t stopped these PSAs from being made. The latest efforts come out of Sweden, where the country’s Patent and Registration Office (PRV) was commissioned by the government to increase public awareness of copyright and help change attitudes surrounding streaming and illegal downloading.

“The purpose is, among other things, to reduce the use of illegal streaming sites and make it easier and safer to find and choose legal options,” PRV says.

“Every year, criminal networks earn millions of dollars from illegal streaming. This money comes from advertising on illegal sites and is used for other criminal activities. The purpose of our film is to inform about this.”

The series of videos show pirates in their supposed natural habitats of beautiful mansions, packed with luxurious items such as indoor pools, fancy staircases, and stacks of money. For some reason (perhaps to depict anonymity, perhaps to suggest something more sinister) the pirates are all dressed in animal masks, such as this one enjoying his Dodge Viper.

The clear suggestion here is that people who visit pirate sites and stream unlicensed content are helping to pay for this guy’s bright green car. The same holds true for his indoor swimming pool, jet bike, and gold chains in the next clip.

While some might have a problem with pirates getting rich from their clicks, it can’t have escaped the targets of these videos that they too are benefiting from the scheme. Granted, hyena-man gets the pool and the Viper, but they get the latest movies. It seems unlikely that pirate streamers refused to watch the copy of Black Panther that leaked onto the web this week (a month before its retail release) on the basis that someone else was getting rich from it.

That being said, most people will probably balk at elements of the full PSA, which suggests that revenue from illegal streaming goes on to fuel other crimes, such as prescription drug offenses.

After reporting piracy cases for more than twelve years, no one at TF has ever seen evidence of this happening with any torrent or streaming site operators. Still, it makes good drama for the full video, embedded below.

“In the film we follow a fictional occupational criminal who gives us a tour of his beautiful villa. He proudly shows up his multi-criminal activity, which was made possible by means of advertising money from his illegal streaming services,” PRV explains.

The dark tone and creepy masks are bound to put some people off but one has to question the effect this kind of video could have on younger people. Do pirates really make mountains of money so huge that they can only be counted by machine? If they do, then it’s a lot less risky than almost any other crime that yields this claimed level of profit.

With that in mind, will this video deter the public or simply encourage people to get involved for some of that big money? We sent a link to the operator of a large pirate site for his considered opinion.

“WTF,” he responded.

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

The intersection of Customer Engagement and Data Science

Post Syndicated from Brent Meyer original https://aws.amazon.com/blogs/messaging-and-targeting/the-intersection-of-customer-engagement-and-data-science/

On the Messaging and Targeting team, we’re constantly inspired by the new and novel ways that customers use our services. For example, last year we took an in-depth look at a customer who built a fully featured email marketing platform based on Amazon SES and other AWS Services.

This week, our friends on the AWS Machine Learning team published a blog post that brings together the worlds of data science and customer engagement. Their solution uses Amazon SageMaker (a platform for building and deploying machine learning models) to create a system that makes purchasing predictions based on customers’ past behaviors. It then uses Amazon Pinpoint to send campaigns to customers based on these predictions.

The blog post is an interesting read that includes a primer on the process of creating a useful Machine Learning solution. It then goes in-depth, discussing the real-world considerations that are involved in implementing the solution.

Take a look at their post, Amazon Pinpoint campaigns driven by machine learning on Amazon SageMaker, on the AWS Machine Learning Blog.

Announcing Local Build Support for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/announcing-local-build-support-for-aws-codebuild/

Today, we’re excited to announce local build support in AWS CodeBuild.

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

In this blog post, I’ll show you how to set up CodeBuild locally to build and test a sample Java application.

By building an application on a local machine you can:

  • Test the integrity and contents of a buildspec file locally.
  • Test and build an application locally before committing.
  • Identify and fix errors quickly from your local development environment.

Prerequisites

In this post, I am using AWS Cloud9 IDE as my development environment.

If you would like to use AWS Cloud9 as your IDE, follow the express setup steps in the AWS Cloud9 User Guide.

The AWS Cloud9 IDE comes with Docker and Git already installed. If you are going to use your laptop or desktop machine as your development environment, install Docker and Git before you start.

Steps to build CodeBuild image locally

Run git clone https://github.com/aws/aws-codebuild-docker-images.git to download this repository to your local machine.

$ git clone https://github.com/aws/aws-codebuild-docker-images.git

Lets build a local CodeBuild image for JDK 8 environment. The Dockerfile for JDK 8 is present in /aws-codebuild-docker-images/ubuntu/java/openjdk-8.

Edit the Dockerfile to remove the last line ENTRYPOINT [“dockerd-entrypoint.sh”] and save the file.

Run cd ubuntu/java/openjdk-8 to change the directory in your local workspace.

Run docker build -t aws/codebuild/java:openjdk-8 . to build the Docker image locally. This command will take few minutes to complete.

$ cd aws-codebuild-docker-images
$ cd ubuntu/java/openjdk-8
$ docker build -t aws/codebuild/java:openjdk-8 .

Steps to setup CodeBuild local agent

Run the following Docker pull command to download the local CodeBuild agent.

$ docker pull amazon/aws-codebuild-local:latest --disable-content-trust=false

Now you have the local agent image on your machine and can run a local build.

Run the following git command to download a sample Java project.

$ git clone https://github.com/karthiksambandam/sample-web-app.git

Steps to use the local agent to build a sample project

Let’s build the sample Java project using the local agent.

Execute the following Docker command to run the local agent and build the sample web app repository you cloned earlier.

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

Note: We need to provide three environment variables namely  IMAGE_NAME, SOURCE and ARTIFACTS.

IMAGE_NAME: The name of your build environment image.

SOURCE: The absolute path to your source code directory.

ARTIFACTS: The absolute path to your artifact output folder.

When you run the sample project, you get a runtime error that says the YAML file does not exist. This is because a buildspec.yml file is not included in the sample web project. AWS CodeBuild requires a buildspec.yml to run a build. For more information about buildspec.yml, see Build Spec Example in the AWS CodeBuild User Guide.

Let’s add a buildspec.yml file with the following content to the sample-web-app folder and then rebuild the project.

version: 0.2

phases:
  build:
    commands:
      - echo Build started on `date`
      - mvn install

artifacts:
  files:
    - target/javawebdemo.war


$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

This time your build should be successful. Upon successful execution, look in the /artifacts folder for the final built artifacts.zip file to validate.

Conclusion:

In this blog post, I showed you how to quickly set up the CodeBuild local agent to build projects right from your local desktop machine or laptop. As you see, local builds can improve developer productivity by helping you identify and fix errors quickly.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ;

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 

Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 

About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

NIST Issues Call for "Lightweight Cryptography" Algorithms

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

This is interesting:

Creating these defenses is the goal of NIST’s lightweight cryptography initiative, which aims to develop cryptographic algorithm standards that can work within the confines of a simple electronic device. Many of the sensors, actuators and other micromachines that will function as eyes, ears and hands in IoT networks will work on scant electrical power and use circuitry far more limited than the chips found in even the simplest cell phone. Similar small electronics exist in the keyless entry fobs to newer-model cars and the Radio Frequency Identification (RFID) tags used to locate boxes in vast warehouses.

All of these gadgets are inexpensive to make and will fit nearly anywhere, but common encryption methods may demand more electronic resources than they possess.

The NSA’s SIMON and SPECK would certainly qualify.

IoT Analytics Now Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/iot-analytics-now-generally-available/

Today, I’m pleased to announce that, as of April 24th 2018, the AWS IoT Analytics service is generally available. Customers can use IoT Analytics to clean, process, encrich, store, and analyze their connected device data at scale. AWS IoT Analytics is now available in US East (N. Virginia), US West (Oregon), US East (Ohio), and EU (Ireland). In November of last year, my colleague Tara Walker wrote an excellent post that walks through some of the features of the AWS IoT Analytics service and Ben Kehoe (an AWS Community Hero and Research Scientist at iRobot) spoke at AWS Re:Invent about replacing iRobot’s existing “rube goldberg machine” for forwarding data into an elasticsearch cluster with AWS IoT Analytics.

Iterating on customer feedback received during the service preview the AWS IoT Analytics team has added a number of new features including the ability to ingest data from external souces using the BatchPutMessage API, the ability to set a data retention policy on stored data, the ability to reprocess existing data, preview pipeline results, and preview messages from channels with the SampleChannelData API.

Let’s cover the core concepts of IoT Analytics and then walk through an example.

AWS IoT Analytics Concepts

AWS IoT Analytics can be broken down into a few simple concepts. For data preparation customers have: Channels, Pipelines, and Data Stores. For analyzing data customers have: Datasets and Notebooks.

Data Preparation

  • Channels are the entry point into IoT Analytics and they collect data from an existing IoT Core MQTT topic or from external sources that send messages to the channel using the Ingestion API. Channels are elastically scalable and consume messages in Binary or JSON format. Channels also immutably store raw device data for easily reprocessing using different logic if your needs change.
  • Pipelines consume messages from channels and allow you to process messages with steps, called activities, such as filtering on attributes, transforming the content of the message by adding or remvoing fields, invoking lambda functions for complex transformations and adding data from external data sources, or even enriching the messages with data from IoT Core. Pipelines output their data to a Data Store.
  • Data Stores are a queryable IoT-optimized data storage solution for the output of your pipelines. Data stores support custom retention periods to optimize costs. When a customer queries a Data Store the result is put into a Dataset.

Data Analytics

  • Datasets are similar to a view in a SQL database. Customers create a dataset by running a query against a data store. Data sets can be generated manually or on a recurring schedule.
  • Notebooks are Amazon SageMaker hosted Jupyter notebooks that let customers analyze their data with custom code and even build or train ML models on the data. IoT Analytics offers several notebook templates with pre-authored models for common IoT use cases such as Predictive Maintenance, Anomaly Detection, Fleet Segmentation, and Forecasting.

Additionally, you can use IoT analytics as a data source for Amazon QuickSight for easy visualizations of your data. You can find pricing information for each of these services on the AWS IoT Analytics Pricing Page.

IoT Analytics Walkthrough

While this walkthrough uses the console everything shown here is equally easy to do with the CLI. When we first navigate to the console we have a helpful guide telling us to build a channel, pipeline, and a data store:

Our first step is to create a channel. I already have some data into an MQTT channel with IoT core so I’ll select that channel. First we’ll name the channel and select a retention period.

Now, I’ll select my IoT Core topic and grab the data. I can also post messages directly into the channel with the PutMessages APIs.

Now that I have a channel my next step is to create a pipeline. To do this I’ll select “Create a pipeline from this channel” from the “Actions” drop down.

Now, I’ll walk through the pipeline wizard giving my pipeline a name and a source.

I’ll select which of the message attributes the pipeline should expect. This can draw from the channel with the sampling API and guess at which attributes are needed or I could upload a specification in JSON.

Next I define the pipeline activities. If I’m dealing with binary data I need a lambda function to first deserialize the message into JSON so the other filter functions can operate on it. I can create filters, calculate attributes based on other attributes, and I can also enrich the message with metadata from IoT core registry.

For now I just want to filter out some messages and make a small transform with a Lambda function.

Finally, I choose or create a data store to output the results of my pipeline.

Now that I have a data store, I can create a view of that data by creating a data set.

I’ll just select all the data from the data store for this dataset but I could also select individual attributes as needed.

I have a data set! I can adjust the cron expression in the schedule to re-run this as frequently or infrequently as I wish.

If I want to create a model from my data I can create a SageMaker powered Jupyter notebook. There are a few templates that are great starting points like anomaly detection or output forecasting.

Here you can see an example of the anomaly detection notebook.

Finally, if I want to create simple visualizations of my data I can use QuickSight to bring in an IoT Analytics data set.

Let Us Know

I’m excited to see what customers build with AWS IoT Analytics. My colleagues on the IoT teams are eager to hear your feedback about the service so please let us know in the comments or on Twitter what features you want to see.

– Randall

Own your own working Pokémon Pokédex!

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/deep-learning-pokedex/

Squeal with delight as your inner Pokémon trainer witnesses the wonder of Adrian Rosebrock’s deep learning Pokédex.

Creating a real-life Pokedex with a Raspberry Pi, Python, and Deep Learning

This video demos a real-like Pokedex, complete with visual recognition, that I created using a Raspberry Pi, Python, and Deep Learning. You can find the entire blog post, including code, using this link: https://www.pyimagesearch.com/2018/04/30/a-fun-hands-on-deep-learning-project-for-beginners-students-and-hobbyists/ Music credit to YouTube user “No Copyright” for providing royalty free music: https://www.youtube.com/watch?v=PXpjqURczn8

The history of Pokémon in 30 seconds

The Pokémon franchise was created by video game designer Satoshi Tajiri in 1995. In the fictional world of Pokémon, Pokémon Trainers explore the vast landscape, catching and training small creatures called Pokémon. To date, there are 802 different types of Pokémon. They range from the ever recognisable Pikachu, a bright yellow electric Pokémon, to the highly sought-after Shiny Charizard, a metallic, playing-card-shaped Pokémon that your mate Alex claims she has in mint condition, but refuses to show you.

Pokemon GIF

In the world of Pokémon, children as young as ten-year-old protagonist and all-round annoyance Ash Ketchum are allowed to leave home and wander the wilderness. There, they hunt vicious, deadly creatures in the hope of becoming a Pokémon Master.

Adrian’s deep learning Pokédex

Adrian is a bit of a deep learning pro, as demonstrated by his Santa/Not Santa detector, which we wrote about last year. For that project, he also provided a great explanation of what deep learning actually is. In a nutshell:

…a subfield of machine learning, which is, in turn, a subfield of artificial intelligence (AI).While AI embodies a large, diverse set of techniques and algorithms related to automatic reasoning (inference, planning, heuristics, etc), the machine learning subfields are specifically interested in pattern recognition and learning from data.

As with his earlier Raspberry Pi project, Adrian uses the Keras deep learning model and the TensorFlow backend, plus a few other packages such as Adrian’s own imutils functions and OpenCV.

Adrian trained a Convolutional Neural Network using Keras on a dataset of 1191 Pokémon images, obtaining 96.84% accuracy. As Adrian explains, this model is able to identify Pokémon via still image and video. It’s perfect for creating a Pokédex – an interactive Pokémon catalogue that should, according to the franchise, be able to identify and read out information on any known Pokémon when captured by camera. More information on model training can be found on Adrian’s blog.

Adrian Rosebeck deep learning pokemon pokedex

For the physical build, a Raspberry Pi 3 with camera module is paired with the Raspberry Pi 7″ touch display to create a portable Pokédex. And while Adrian comments that the same result can be achieved using your home computer and a webcam, that’s not how Adrian rolls as a Raspberry Pi fan.

Adrian Rosebeck deep learning pokemon pokedex

Plus, the smaller size of the Pi is perfect for one of you to incorporate this deep learning model into a 3D-printed Pokédex for ultimate Pokémon glory, pretty please, thank you.

Adrian Rosebeck deep learning pokemon pokedex

Adrian has gone into impressive detail about how the project works and how you can create your own on his blog, pyimagesearch. So if you’re interested in learning more about deep learning, and making your own Pokédex, be sure to visit.

The post Own your own working Pokémon Pokédex! appeared first on Raspberry Pi.

#FixCopyright #SaveTheLink #CensorshipMachines

Post Syndicated from nellyo original https://nellyo.wordpress.com/2018/04/28/fixcopyright-savethelink-censorshipmachines/

На 26 април 2018 г. учени от 25 водещи изследователски института за интелектуална собственост в Европа  публикуваха отворено писмо, изразяващо сериозни опасения относно  предложената директива за авторско  право.

Според авторите след балансиран проектодоклад на докладчика на Европейския парламент Тереза ​​Комодини (Доклад Comodini,  10 март 2017 г.) неотдавнашни текстове от новия докладчик Axel Voss и българското председателство  на Съвета на Европейският съюз изглежда водят към компромис, който не отговаря на целите на директивата за подобряване на избора, достъпа и справедливостта в цифровата среда.

Има научен консенсус
– че предложеното изключение   по чл. 3 няма да постигне целта си да стимулира иновациите и научните изследвания, ако се ограничи до определени организации,
– че предложенията за право на  издателите по чл. 11 ще благоприятстват по-скоро интересите на издателските интереси в пресата, отколкото иновативната журналистика,
– и че предложенията за чл. 13 застрашават ползата от  Директивата за електронната търговия (2000/31 / ЕО), която споделя отговорността за прилагането между носителите на авторски права и доставчиците на услуги.

169 представители на академичните среди предупреждават за недобре замислените планове за въвеждането на ново право на интелектуална собственост (сродно право за издателите). Те напомнят, че широк спектър от заинтересовани групи вече представи становища, че Директивата за авторското право в настоящия си вид няма да бъде в обществен интерес.

Близо 150 неправителствени организации също се противопоставят на актуалния проект. Според тях предопределено е, че транспониране на  законодателство в този вид ще е  кошмар. Същото се отнася и до проверката на съответствието на директивата с  Хартата на основните права на ЕС и Бернската конвенция. Отворено писмо

По стечение на обстоятелствата името на България  ще остане свързано с тази директива. Ако това ни интересува.

*

На 27 април 2018 г. (вчера) по план в COREPER се обсъжда  предоставяне на мандат за преговори с Европейския парламент по Директивата за авторското право.  

 (текстът)

Kакво е решил COREPER? според Юлия Реда не е даден мандат:

Continued: the answers to your questions for Eben Upton

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/eben-q-a-2/

Last week, we shared the first half of our Q&A with Raspberry Pi Trading CEO and Raspberry Pi creator Eben Upton. Today we follow up with all your other questions, including your expectations for a Raspberry Pi 4, Eben’s dream add-ons, and whether we really could go smaller than the Zero.

Live Q&A with Eben Upton, creator of the Raspberry Pi

Get your questions to us now using #AskRaspberryPi on Twitter

With internet security becoming more necessary, will there be automated versions of VPN on an SD card?

There are already third-party tools which turn your Raspberry Pi into a VPN endpoint. Would we do it ourselves? Like the power button, it’s one of those cases where there are a million things we could do and so it’s more efficient to let the community get on with it.

Just to give a counterexample, while we don’t generally invest in optimising for particular use cases, we did invest a bunch of money into optimising Kodi to run well on Raspberry Pi, because we found that very large numbers of people were using it. So, if we find that we get half a million people a year using a Raspberry Pi as a VPN endpoint, then we’ll probably invest money into optimising it and feature it on the website as we’ve done with Kodi. But I don’t think we’re there today.

Have you ever seen any Pis running and doing important jobs in the wild, and if so, how does it feel?

It’s amazing how often you see them driving displays, for example in radio and TV studios. Of course, it feels great. There’s something wonderful about the geographic spread as well. The Raspberry Pi desktop is quite distinctive, both in its previous incarnation with the grey background and logo, and the current one where we have Greg Annandale’s road picture.

The PIXEL desktop on Raspberry Pi

And so it’s funny when you see it in places. Somebody sent me a video of them teaching in a classroom in rural Pakistan and in the background was Greg’s picture.

Raspberry Pi 4!?!

There will be a Raspberry Pi 4, obviously. We get asked about it a lot. I’m sticking to the guidance that I gave people that they shouldn’t expect to see a Raspberry Pi 4 this year. To some extent, the opportunity to do the 3B+ was a surprise: we were surprised that we’ve been able to get 200MHz more clock speed, triple the wireless and wired throughput, and better thermals, and still stick to the $35 price point.

We’re up against the wall from a silicon perspective; we’re at the end of what you can do with the 40nm process. It’s not that you couldn’t clock the processor faster, or put a larger processor which can execute more instructions per clock in there, it’s simply about the energy consumption and the fact that you can’t dissipate the heat. So we’ve got to go to a smaller process node and that’s an order of magnitude more challenging from an engineering perspective. There’s more effort, more risk, more cost, and all of those things are challenging.

With 3B+ out of the way, we’re going to start looking at this now. For the first six months or so we’re going to be figuring out exactly what people want from a Raspberry Pi 4. We’re listening to people’s comments about what they’d like to see in a new Raspberry Pi, and I’m hoping by early autumn we should have an idea of what we want to put in it and a strategy for how we might achieve that.

Could you go smaller than the Zero?

The challenge with Zero as that we’re periphery-limited. If you run your hand around the unit, there is no edge of that board that doesn’t have something there. So the question is: “If you want to go smaller than Zero, what feature are you willing to throw out?”

It’s a single-sided board, so you could certainly halve the PCB area if you fold the circuitry and use both sides, though you’d have to lose something. You could give up some GPIO and go back to 26 pins like the first Raspberry Pi. You could give up the camera connector, you could go to micro HDMI from mini HDMI. You could remove the SD card and just do USB boot. I’m inventing a product live on air! But really, you could get down to two thirds and lose a bunch of GPIO – it’s hard to imagine you could get to half the size.

What’s the one feature that you wish you could outfit on the Raspberry Pi that isn’t cost effective at this time? Your dream feature.

Well, more memory. There are obviously technical reasons why we don’t have more memory on there, but there are also market reasons. People ask “why doesn’t the Raspberry Pi have more memory?”, and my response is typically “go and Google ‘DRAM price’”. We’re used to the price of memory going down. And currently, we’re going through a phase where this has turned around and memory is getting more expensive again.

Machine learning would be interesting. There are machine learning accelerators which would be interesting to put on a piece of hardware. But again, they are not going to be used by everyone, so according to our method of pricing what we might add to a board, machine learning gets treated like a $50 chip. But that would be lovely to do.

Which citizen science projects using the Pi have most caught your attention?

I like the wildlife camera projects. We live out in the countryside in a little village, and we’re conscious of being surrounded by nature but we don’t see a lot of it on a day-to-day basis. So I like the nature cam projects, though, to my everlasting shame, I haven’t set one up yet. There’s a range of them, from very professional products to people taking a Raspberry Pi and a camera and putting them in a plastic box. So those are good fun.

Raspberry Shake seismometer

The Raspberry Shake seismometer

And there’s Meteor Pi from the Cambridge Science Centre, that’s a lot of fun. And the seismometer Raspberry Shake – that sort of thing is really nice. We missed the recent South Wales earthquake; perhaps we should set one up at our Californian office.

How does it feel to go to bed every day knowing you’ve changed the world for the better in such a massive way?

What feels really good is that when we started this in 2006 nobody else was talking about it, but now we’re part of a very broad movement.

We were in a really bad way: we’d seen a collapse in the number of applicants applying to study Computer Science at Cambridge and elsewhere. In our view, this reflected a move away from seeing technology as ‘a thing you do’ to seeing it as a ‘thing that you have done to you’. It is problematic from the point of view of the economy, industry, and academia, but most importantly it damages the life prospects of individual children, particularly those from disadvantaged backgrounds. The great thing about STEM subjects is that you can’t fake being good at them. There are a lot of industries where your Dad can get you a job based on who he knows and then you can kind of muddle along. But if your dad gets you a job building bridges and you suck at it, after the first or second bridge falls down, then you probably aren’t going to be building bridges anymore. So access to STEM education can be a great driver of social mobility.

By the time we were launching the Raspberry Pi in 2012, there was this wonderful movement going on. Code Club, for example, and CoderDojo came along. Lots of different ways of trying to solve the same problem. What feels really, really good is that we’ve been able to do this as part of an enormous community. And some parts of that community became part of the Raspberry Pi Foundation – we merged with Code Club, we merged with CoderDojo, and we continue to work alongside a lot of these other organisations. So in the two seconds it takes me to fall asleep after my face hits the pillow, that’s what I think about.

We’re currently advertising a Programme Manager role in New Delhi, India. Did you ever think that Raspberry Pi would be advertising a role like this when you were bringing together the Foundation?

No, I didn’t.

But if you told me we were going to be hiring somewhere, India probably would have been top of my list because there’s a massive IT industry in India. When we think about our interaction with emerging markets, India, in a lot of ways, is the poster child for how we would like it to work. There have already been some wonderful deployments of Raspberry Pi, for example in Kerala, without our direct involvement. And we think we’ve got something that’s useful for the Indian market. We have a product, we have clubs, we have teacher training. And we have a body of experience in how to teach people, so we have a physical commercial product as well as a charitable offering that we think are a good fit.

It’s going to be massive.

What is your favourite BBC type-in listing?

There was a game called Codename: Druid. There is a famous game called Codename: Droid which was the sequel to Stryker’s Run, which was an awesome, awesome game. And there was a type-in game called Codename: Druid, which was at the bottom end of what you would consider a commercial game.

codename druid

And I remember typing that in. And what was really cool about it was that the next month, the guy who wrote it did another article that talks about the memory map and which operating system functions used which bits of memory. So if you weren’t going to do disc access, which bits of memory could you trample on and know the operating system would survive.

babbage versus bugs Raspberry Pi annual

See the full listing for Babbage versus Bugs in the Raspberry Pi 2018 Annual

I still like type-in listings. The Raspberry Pi 2018 Annual has a type-in listing that I wrote for a Babbage versus Bugs game. I will say that’s not the last type-in listing you will see from me in the next twelve months. And if you download the PDF, you could probably copy and paste it into your favourite text editor to save yourself some time.

The post Continued: the answers to your questions for Eben Upton appeared first on Raspberry Pi.

MPAA Chief Says Fighting Piracy Remains “Top Priority”

Post Syndicated from Andy original https://torrentfreak.com/mpaa-chief-says-fighting-piracy-remains-top-priority-180425/

After several high-profile years at the helm of the movie industry’s most powerful lobbying group, last year saw the departure of Chris Dodd from the role of Chairman and CEO at the MPAA.

The former Senator, who earned more than $3.5m a year championing the causes of the major Hollywood studios since 2011, was immediately replaced by another political heavyweight.

Charles Rivkin, who took up his new role September 5, 2017, previously served as Assistant Secretary of State for Economic and Business Affairs in the Obama administration. With an underperforming domestic box office year behind him fortunately overshadowed by massive successes globally, this week he spoke before US movie exhibitors for the first time at CinemaCon in Las Vegas.

“Globally, we hit a record high of $40.6 billion at the box office. Domestically, our $11.1 billion box office was slightly down from the 2016 record. But it exactly matched the previous high from 2015. And it was the second highest total in the past decade,” Rivkin said.

“But it exactly matched the previous high from 2015. And it was the second highest total in the past decade.”

Rivkin, who spent time as President and CEO of The Jim Henson Company, told those in attendance that he shares a deep passion for the movie industry and looks forward optimistically to the future, a future in which content is secured from those who intend on sharing it for free.

“Making sure our creative works are valued and protected is one of the most important things we can do to keep that industry heartbeat strong. At the Henson Company, and WildBrain, I learned just how much intellectual property affects everyone. Our entire business model depended on our ability to license Kermit the Frog, Miss Piggy, and the Muppets and distribute them across the globe,” Rivkin said.

“I understand, on a visceral level, how important copyright is to any creative business and in particular our country’s small and medium enterprises – which are the backbone of the American economy. As Chairman and CEO of the MPAA, I guarantee you that fighting piracy in all forms remains our top priority.”

That tackling piracy is high on the MPAA’s agenda won’t comes as a surprise but at least in terms of the numbers of headlines plastered over the media, high-profile anti-piracy action has been somewhat lacking in recent years.

With lawsuits against torrent sites seemingly a thing of the past and a faltering Megaupload case that will conclude who-knows-when, the MPAA has taken a broader view, seeking partnerships with sometimes rival content creators and distributors, each with a shared desire to curtail illicit media.

“One of the ways that we’re already doing that is through the Alliance for Creativity and Entertainment – or ACE as we call it,” Rivkin said.

“This is a coalition of 30 leading global content creators, including the MPAA’s six member studios as well as Netflix, and Amazon. We work together as a powerful team to ensure our stories are seen as they were intended to be, and that their creators are rewarded for their hard work.”

Announced in June 2017, ACE has become a united anti-piracy powerhouse for a huge range of entertainment industry groups, encompassing the likes of CBS, HBO, BBC, Sky, Bell Canada, CBS, Hulu, Lionsgate, Foxtel and Village Roadshow, to name a few.

The coalition was announced by former MPAA Chief Chris Dodd and now, with serious financial input from all companies involved, appears to be picking its fights carefully, focusing on the growing problem of streaming piracy centered around misuse of Kodi and similar platforms.

From threatening relatively small-time producers and distributors of third-party addons and builds (1,2,3), ACE is also attempting to make its mark among the profiteers.

The group now has several lawsuits underway in the United States against people selling piracy-enabled IPTV boxes including Tickbox, Dragon Box, and during the last week, Set TV.

With these important cases pending, Rivkin offered assurances that his organization remains committed to anti-piracy enforcement and he thanked exhibitors for their efforts to prevent people quickly running away with copies of the latest releases.

“I am grateful to all of you for recognizing what is at stake, and for working with us to protect creativity, such as fighting the use of illegal camcorders in theaters,” he said.

“Protecting our creativity isn’t only a fundamental right. It’s an economic necessity, for us and all creative economies. Film and television are among the most valuable – and most impactful – exports we have.

Thus far at least, Rivkin has a noticeably less aggressive tone on piracy than his predecessor Chris Dodd but it’s unlikely that will be mistaken for weakness among pirates, nor should it. The MPAA isn’t known for going soft on pirates and it certainly won’t be changing course anytime soon.

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