Tag Archives: BT

DNS over HTTPS in Firefox

Post Syndicated from corbet original https://lwn.net/Articles/756262/rss

The Mozilla blog has an
describing the addition of DNS over HTTPS (DoH) as an optional
feature in the Firefox browser. “DoH support has been added to
Firefox 62 to improve the way Firefox interacts with DNS. DoH uses
encrypted networking to obtain DNS information from a server that is
configured within Firefox. This means that DNS requests sent to the DoH
cloud server are encrypted while old style DNS requests are not
” The configured server is hosted by Cloudflare, which
has posted this
privacy agreement
about the service.

[$] Stratis: Easy local storage management for Linux

Post Syndicated from jake original https://lwn.net/Articles/755454/rss

Stratis is a new local
storage-management solution for Linux. It can be compared to
ZFS, Btrfs, or LVM. Its focus is on simplicity of concepts and ease of use,
while giving users access to advanced storage features. Internally,
Stratis’s implementation favors tight integration of existing
components instead of the fully-integrated, in-kernel approach that ZFS and
Btrfs use. This has benefits and drawbacks for Stratis, but also greatly
decreases the overall time needed to develop a useful and stable initial
version, which can then be a base for further improvement in later
versions. Subscribers can read on for an introduction to Stratis, by guest
author (and Stratis team lead at Red Hat) Andy Grover.

The devil wears Pravda

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/the-devil-wears-pravda.html

Classic Bond villain, Elon Musk, has a new plan to create a website dedicated to measuring the credibility and adherence to “core truth” of journalists. He is, without any sense of irony, going to call this “Pravda”. This is not simply wrong but evil.

Musk has a point. Journalists do suck, and many suck consistently. I see this in my own industry, cybersecurity, and I frequently criticize them for their suckage.

But what he’s doing here is not correcting them when they make mistakes (or what Musk sees as mistakes), but questioning their legitimacy. This legitimacy isn’t measured by whether they follow established journalism ethics, but whether their “core truths” agree with Musk’s “core truths”.

An example of the problem is how the press fixates on Tesla car crashes due to its “autopilot” feature. Pretty much every autopilot crash makes national headlines, while the press ignores the other 40,000 car crashes that happen in the United States each year. Musk spies on Tesla drivers (hello, classic Bond villain everyone) so he can see the dip in autopilot usage every time such a news story breaks. He’s got good reason to be concerned about this.

He argues that autopilot is safer than humans driving, and he’s got the statistics and government studies to back this up. Therefore, the press’s fixation on Tesla crashes is illegitimate “fake news”, titillating the audience with distorted truth.

But here’s the thing: that’s still only Musk’s version of the truth. Yes, on a mile-per-mile basis, autopilot is safer, but there’s nuance here. Autopilot is used primarily on freeways, which already have a low mile-per-mile accident rate. People choose autopilot only when conditions are incredibly safe and drivers are unlikely to have an accident anyway. Musk is therefore being intentionally deceptive comparing apples to oranges. Autopilot may still be safer, it’s just that the numbers Musk uses don’t demonstrate this.

And then there is the truth calling it “autopilot” to begin with, because it isn’t. The public is overrating the capabilities of the feature. It’s little different than “lane keeping” and “adaptive cruise control” you can now find in other cars. In many ways, the technology is behind — my Tesla doesn’t beep at me when a pedestrian walks behind my car while backing up, but virtually every new car on the market does.

Yes, the press unduly covers Tesla autopilot crashes, but Musk has only himself to blame by unduly exaggerating his car’s capabilities by calling it “autopilot”.

What’s “core truth” is thus rather difficult to obtain. What the press satisfies itself with instead is smaller truths, what they can document. The facts are in such cases that the accident happened, and they try to get Tesla or Musk to comment on it.

What you can criticize a journalist for is therefore not “core truth” but whether they did journalism correctly. When such stories criticize “autopilot”, but don’t do their diligence in getting Tesla’s side of the story, then that’s a violation of journalistic practice. When I criticize journalists for their poor handling of stories in my industry, I try to focus on which journalistic principles they get wrong. For example, the NYTimes reporters do a lot of stories quoting anonymous government sources in clear violation of journalistic principles.

If “credibility” is the concern, then it’s the classic Bond villain here that’s the problem: Musk himself. His track record on business statements is abysmal. For example, when he announced the Model 3 he claimed production targets that every Wall Street analyst claimed were absurd. He didn’t make those targets, he didn’t come close. Model 3 production is still lagging behind Musk’s twice adjusted targets.


So who has a credibility gap here, the press, or Musk himself?

Not only is Musk’s credibility problem ironic, so is the name he chose, “Pravada”, the Russian word for truth that was the name of the Soviet Union Communist Party’s official newspaper. This is so absurd this has to be a joke, yet Musk claims to be serious about all this.

Yes, the press has a lot of problems, and if Musk were some journalism professor concerned about journalists meeting the objective standards of their industry (e.g. abusing anonymous sources), then this would be a fine thing. But it’s not. It’s Musk who is upset the press’s version of “core truth” does not agree with his version — a version that he’s proven time and time again differs from “real truth”.

Just in case Musk is serious, I’ve already registered “www.antipravda.com” to start measuring the credibility of statements by billionaire playboy CEOs. Let’s see who blinks first.

I stole the title, with permission, from this tweet:

RFC: LWN’s draft updated privacy policy

Post Syndicated from corbet original https://lwn.net/Articles/755089/rss

It is the season for web sites to be updating their privacy policies and
obtaining consent from their users for whatever data they collect. LWN,
being short of staff with the time or interest to work in this area, is
rather late to this game. The first step is an updated
privacy policy, which we’re now putting out for review. Little has changed
from the current version; we still don’t
collect much data, share data with others, or attempt to
monetize what we have in any way. We would like to ask interested readers
to have a look and let us know about any potential problems they see.

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

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

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

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

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

Instance NamevCPUsRAMLocal StorageEBS BandwidthNetwork Bandwidth
c5d.large24 GiB1 x 50 GB NVMe SSDUp to 2.25 GbpsUp to 10 Gbps
c5d.xlarge48 GiB1 x 100 GB NVMe SSDUp to 2.25 GbpsUp to 10 Gbps
c5d.2xlarge816 GiB1 x 225 GB NVMe SSDUp to 2.25 GbpsUp to 10 Gbps
c5d.4xlarge1632 GiB1 x 450 GB NVMe SSD2.25 GbpsUp to 10 Gbps
c5d.9xlarge3672 GiB1 x 900 GB NVMe SSD4.5 Gbps10 Gbps
c5d.18xlarge72144 GiB2 x 900 GB NVMe SSD9 Gbps25 Gbps

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

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

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

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

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

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

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


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

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 ControlTim 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.



Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/754653/rss

Security updates have been issued by CentOS (dhcp), Debian (xen), Fedora (dhcp, flac, kubernetes, leptonica, libgxps, LibRaw, matrix-synapse, mingw-LibRaw, mysql-mmm, patch, seamonkey, webkitgtk4, and xen), Mageia (389-ds-base, exempi, golang, graphite2, libpam4j, libraw, libsndfile, libtiff, perl, quassel, spring-ldap, util-linux, and wget), Oracle (dhcp and kernel), Red Hat (389-ds-base, chromium-browser, dhcp, docker-latest, firefox, kernel-alt, libvirt, qemu-kvm, redhat-vertualization-host, rh-haproxy18-haproxy, and rhvm-appliance), Scientific Linux (389-ds-base, dhcp, firefox, libvirt, and qemu-kvm), and Ubuntu (poppler).

Airline Ticket Fraud

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

New research: “Leaving on a jet plane: the trade in fraudulently obtained airline tickets:”

Abstract: Every day, hundreds of people fly on airline tickets that have been obtained fraudulently. This crime script analysis provides an overview of the trade in these tickets, drawing on interviews with industry and law enforcement, and an analysis of an online blackmarket. Tickets are purchased by complicit travellers or resellers from the online blackmarket. Victim travellers obtain tickets from fake travel agencies or malicious insiders. Compromised credit cards used to be the main method to purchase tickets illegitimately. However, as fraud detection systems improved, offenders displaced to other methods, including compromised loyalty point accounts, phishing, and compromised business accounts. In addition to complicit and victim travellers, fraudulently obtained tickets are used for transporting mules, and for trafficking and smuggling. This research details current prevention approaches, and identifies additional interventions, aimed at the act, the actor, and the marketplace.

Blog post.

Supply-Chain Security

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

Earlier this month, the Pentagon stopped selling phones made by the Chinese companies ZTE and Huawei on military bases because they might be used to spy on their users.

It’s a legitimate fear, and perhaps a prudent action. But it’s just one instance of the much larger issue of securing our supply chains.

All of our computerized systems are deeply international, and we have no choice but to trust the companies and governments that touch those systems. And while we can ban a few specific products, services or companies, no country can isolate itself from potential foreign interference.

In this specific case, the Pentagon is concerned that the Chinese government demanded that ZTE and Huawei add “backdoors” to their phones that could be surreptitiously turned on by government spies or cause them to fail during some future political conflict. This tampering is possible because the software in these phones is incredibly complex. It’s relatively easy for programmers to hide these capabilities, and correspondingly difficult to detect them.

This isn’t the first time the United States has taken action against foreign software suspected to contain hidden features that can be used against us. Last December, President Trump signed into law a bill banning software from the Russian company Kaspersky from being used within the US government. In 2012, the focus was on Chinese-made Internet routers. Then, the House Intelligence Committee concluded: “Based on available classified and unclassified information, Huawei and ZTE cannot be trusted to be free of foreign state influence and thus pose a security threat to the United States and to our systems.”

Nor is the United States the only country worried about these threats. In 2014, China reportedly banned antivirus products from both Kaspersky and the US company Symantec, based on similar fears. In 2017, the Indian government identified 42 smartphone apps that China subverted. Back in 1997, the Israeli company Check Point was dogged by rumors that its government added backdoors into its products; other of that country’s tech companies have been suspected of the same thing. Even al-Qaeda was concerned; ten years ago, a sympathizer released the encryption software Mujahedeen Secrets, claimed to be free of Western influence and backdoors. If a country doesn’t trust another country, then it can’t trust that country’s computer products.

But this trust isn’t limited to the country where the company is based. We have to trust the country where the software is written — and the countries where all the components are manufactured. In 2016, researchers discovered that many different models of cheap Android phones were sending information back to China. The phones might be American-made, but the software was from China. In 2016, researchers demonstrated an even more devious technique, where a backdoor could be added at the computer chip level in the factory that made the chips ­ without the knowledge of, and undetectable by, the engineers who designed the chips in the first place. Pretty much every US technology company manufactures its hardware in countries such as Malaysia, Indonesia, China and Taiwan.

We also have to trust the programmers. Today’s large software programs are written by teams of hundreds of programmers scattered around the globe. Backdoors, put there by we-have-no-idea-who, have been discovered in Juniper firewalls and D-Link routers, both of which are US companies. In 2003, someone almost slipped a very clever backdoor into Linux. Think of how many countries’ citizens are writing software for Apple or Microsoft or Google.

We can go even farther down the rabbit hole. We have to trust the distribution systems for our hardware and software. Documents disclosed by Edward Snowden showed the National Security Agency installing backdoors into Cisco routers being shipped to the Syrian telephone company. There are fake apps in the Google Play store that eavesdrop on you. Russian hackers subverted the update mechanism of a popular brand of Ukrainian accounting software to spread the NotPetya malware.

In 2017, researchers demonstrated that a smartphone can be subverted by installing a malicious replacement screen.

I could go on. Supply-chain security is an incredibly complex problem. US-only design and manufacturing isn’t an option; the tech world is far too internationally interdependent for that. We can’t trust anyone, yet we have no choice but to trust everyone. Our phones, computers, software and cloud systems are touched by citizens of dozens of different countries, any one of whom could subvert them at the demand of their government. And just as Russia is penetrating the US power grid so they have that capability in the event of hostilities, many countries are almost certainly doing the same thing at the consumer level.

We don’t know whether the risk of Huawei and ZTE equipment is great enough to warrant the ban. We don’t know what classified intelligence the United States has, and what it implies. But we do know that this is just a minor fix for a much larger problem. It’s doubtful that this ban will have any real effect. Members of the military, and everyone else, can still buy the phones. They just can’t buy them on US military bases. And while the US might block the occasional merger or acquisition, or ban the occasional hardware or software product, we’re largely ignoring that larger issue. Solving it borders on somewhere between incredibly expensive and realistically impossible.

Perhaps someday, global norms and international treaties will render this sort of device-level tampering off-limits. But until then, all we can do is hope that this particular arms race doesn’t get too far out of control.

This essay previously appeared in the Washington Post.

Creating a 1.3 Million vCPU Grid on AWS using EC2 Spot Instances and TIBCO GridServer

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/creating-a-1-3-million-vcpu-grid-on-aws-using-ec2-spot-instances-and-tibco-gridserver/

Many of my colleagues are fortunate to be able to spend a good part of their day sitting down with and listening to our customers, doing their best to understand ways that we can better meet their business and technology needs. This information is treated with extreme care and is used to drive the roadmap for new services and new features.

AWS customers in the financial services industry (often abbreviated as FSI) are looking ahead to the Fundamental Review of Trading Book (FRTB) regulations that will come in to effect between 2019 and 2021. Among other things, these regulations mandate a new approach to the “value at risk” calculations that each financial institution must perform in the four hour time window after trading ends in New York and begins in Tokyo. Today, our customers report this mission-critical calculation consumes on the order of 200,000 vCPUs, growing to between 400K and 800K vCPUs in order to meet the FRTB regulations. While there’s still some debate about the magnitude and frequency with which they’ll need to run this expanded calculation, the overall direction is clear.

Building a Big Grid
In order to make sure that we are ready to help our FSI customers meet these new regulations, we worked with TIBCO to set up and run a proof of concept grid in the AWS Cloud. The periodic nature of the calculation, along with the amount of processing power and storage needed to run it to completion within four hours, make it a great fit for an environment where a vast amount of cost-effective compute power is available on an on-demand basis.

Our customers are already using the TIBCO GridServer on-premises and want to use it in the cloud. This product is designed to run grids at enterprise scale. It runs apps in a virtualized fashion, and accepts requests for resources, dynamically provisioning them on an as-needed basis. The cloud version supports Amazon Linux as well as the PostgreSQL-compatible edition of Amazon Aurora.

Working together with TIBCO, we set out to create a grid that was substantially larger than the current high-end prediction of 800K vCPUs, adding a 50% safety factor and then rounding up to reach 1.3 million vCPUs (5x the size of the largest on-premises grid). With that target in mind, the account limits were raised as follows:

  • Spot Instance Limit – 120,000
  • EBS Volume Limit – 120,000
  • EBS Capacity Limit – 2 PB

If you plan to create a grid of this size, you should also bring your friendly local AWS Solutions Architect into the loop as early as possible. They will review your plans, provide you with architecture guidance, and help you to schedule your run.

Running the Grid
We hit the Go button and launched the grid, watching as it bid for and obtained Spot Instances, each of which booted, initialized, and joined the grid within two minutes. The test workload used the Strata open source analytics & market risk library from OpenGamma and was set up with their assistance.

The grid grew to 61,299 Spot Instances (1.3 million vCPUs drawn from 34 instance types spanning 3 generations of EC2 hardware) as planned, with just 1,937 instances reclaimed and automatically replaced during the run, and cost $30,000 per hour to run, at an average hourly cost of $0.078 per vCPU. If the same instances had been used in On-Demand form, the hourly cost to run the grid would have been approximately $93,000.

Despite the scale of the grid, prices for the EC2 instances did not move during the bidding process. This is due to the overall size of the AWS Cloud and the smooth price change model that we launched late last year.

To give you a sense of the compute power, we computed that this grid would have taken the #1 position on the TOP 500 supercomputer list in November 2007 by a considerable margin, and the #2 position in June 2008. Today, it would occupy position #360 on the list.

I hope that you enjoyed this AWS success story, and that it gives you an idea of the scale that you can achieve in the cloud!


2018-05-03 python, multiprocessing, thread-ове и забивания

Post Syndicated from Vasil Kolev original https://vasil.ludost.net/blog/?p=3384

Всеки ден се убеждавам, че нищо не работи.

Открих забавен проблем с python и multiprocessing, който в момента още не мога да реша чий проблем е (в крайна сметка ще се окаже мой). Отне ми прилично количество време да го хвана и си струва да го разкажа.

Малко предистория: ползваме influxdb, в което тъпчем бая секундни данни, които после предъвкваме до минутни. InfluxDB има continuous queries, които вършат тази работа – на някакъв интервал от време хващат новите данни и ги сгъват. Тези заявки имаха няколко проблема:
– не се оправят с попълване на стари данни;
– изпълняват се рядко и минутните данни изостават;
– изпълняват се в общи линии в един thread, което кара минутните данни да изостават още повече (в нашия случай преди да ги сменим с около 12 часа).

Хванаха ме дяволите и си написах просто демонче на python, което да събира информация за различните бази какви данни могат да се сгънат, и паралелно да попълва данните. Работи в общи линии по следния начин:
– взима списък с базите данни
– пуска през multiprocessing-а да се събере за всяка база какви заявки трябва да се пуснат, на база на какви measurement-и има и докога са минутните и секундните данни в тях;
– пуска през multiprocessing-а събраните от предния pass заявки
– и така до края на света (или докато зависне).

След като навакса за няколко часа, успяваше да държи минутните данни в рамките на няколко минути от последните секундни данни, което си беше сериозно подобрение на ситуацията. Единственият проблем беше, че от време на време спираше да process-ва и увисваше.

Днес намерих време да го прегледам внимателно какво му се случва. Процесът изглежда като един parent и 5 fork()-нати child-а, като:
Parent-а спи във futex 0x22555a0;
Child 18455 във futex 0x7fdbfa366000;
Child 18546 read
Child 18457 във futex 0x7fdbfa366000
Child 18461 във futex 0x7fdbfa366000
Child 18462 във futex 0x7fdbfa366000
Child 18465 във futex 0x7fdbf908c2c0

Това не беше особено полезно, и се оказа, че стандартния python debugger (pdb) не може да се закача за съществуващи процеси, но за сметка на това gdb с подходящи debug символи може, и може да дава доста полезна информация. По този начин открих, че parent-а чака един child да приключи работата си:

#11 PyEval_EvalFrameEx (
[email protected]=Frame 0x235fb80, for file /usr/lib64/python2.7/multiprocessing/pool.py, line 543, in wait (self== 1525137960000000000 AND time < 1525138107000000000 GROUP BY time(1m), * fill(linear)\' in a read only context, please use a POST request instead', u'level': u'warning'}], u'statement_id': 0}]}, None], _callback=None, _chunksize=1, _number_left=1, _ready=False, _success=True, _cond=<_Condition(_Verbose__verbose=False, _Condition__lock=, acquire=, _Condition__waiters=[], release=) at remote 0x7fdbe0015310>, _job=45499, _cache={45499: < ...>}) a...(truncated), [email protected]=0) at /usr/src/debug/Python-2.7.5/Python/ceval.c:3040

Като в pool.py около ред 543 има следното:

class ApplyResult(object):


def wait(self, timeout=None):
if not self._ready:

Първоначално си мислех, че 18546 очаква да прочете нещо от грешното място, но излезе, че това е child-а, който е спечелил състезанието за изпълняване на следващата задача и чака да му я дадат (което изглежда се раздава през futex 0x7fdbfa366000). Един от child-овете обаче чака в друг lock:

(gdb) bt
#0 __lll_lock_wait () at ../nptl/sysdeps/unix/sysv/linux/x86_64/lowlevellock.S:135
#1 0x00007fdbf9b68dcb in _L_lock_812 () from /lib64/libpthread.so.0
#2 0x00007fdbf9b68c98 in __GI___pthread_mutex_lock ([email protected]=0x7fdbf908c2c0 ) at ../nptl/pthread_mutex_lock.c:79
#3 0x00007fdbf8e846ea in _nss_files_gethostbyname4_r ([email protected]=0x233fa44 "localhost", [email protected]=0x7fdbecfcb8e0, [email protected]=0x7fdbecfcb340 "hZ \372\333\177",
[email protected]=1064, [email protected]=0x7fdbecfcb8b0, [email protected]=0x7fdbecfcb910, [email protected]=0x0) at nss_files/files-hosts.c:381
#4 0x00007fdbf9170ed8 in gaih_inet (name=, [email protected]=0x233fa44 "localhost", service=, req=req[email protected]=0x7fdbecfcbb90, [email protected]=0x7fdbecfcb9f0,
[email protected]=0x7fdbecfcb9e0) at ../sysdeps/posix/getaddrinfo.c:877
#5 0x00007fdbf91745cd in __GI_getaddrinfo ([email protected]=0x233fa44 "localhost", [email protected]=0x7fdbecfcbbc0 "8086", [email protected]=0x7fdbecfcbb90, [email protected]=0x7fdbecfcbb78)
at ../sysdeps/posix/getaddrinfo.c:2431
#6 0x00007fdbeed8760d in socket_getaddrinfo (self=
, args=) at /usr/src/debug/Python-2.7.5/Modules/socketmodule.c:4193
#7 0x00007fdbf9e5fbb0 in call_function (oparg=
, pp_stack=0x7fdbecfcbd10) at /usr/src/debug/Python-2.7.5/Python/ceval.c:4408
#8 PyEval_EvalFrameEx (
[email protected]=Frame 0x7fdbe8013350, for file /usr/lib/python2.7/site-packages/urllib3/util/connection.py, line 64, in create_connection (address=('localhost', 8086), timeout=3000, source_address=None, socket_options=[(6, 1, 1)], host='localhost', port=8086, err=None), [email protected]=0) at /usr/src/debug/Python-2.7.5/Python/ceval.c:3040

(gdb) frame 3
#3 0x00007fdbf8e846ea in _nss_files_gethostbyname4_r ([email protected]=0x233fa44 "localhost", [email protected]=0x7fdbecfcb8e0, [email protected]=0x7fdbecfcb340 "hZ \372\333\177",
[email protected]=1064, [email protected]=0x7fdbecfcb8b0, [email protected]=0x7fdbecfcb910, [email protected]=0x0) at nss_files/files-hosts.c:381
381 __libc_lock_lock (lock);
(gdb) list
376 enum nss_status
377 _nss_files_gethostbyname4_r (const char *name, struct gaih_addrtuple **pat,
378 char *buffer, size_t buflen, int *errnop,
379 int *herrnop, int32_t *ttlp)
380 {
381 __libc_lock_lock (lock);
383 /* Reset file pointer to beginning or open file. */
384 enum nss_status status = internal_setent (keep_stream);

Или в превод – опитваме се да вземем стандартния lock, който libc-то използва за да си пази reentrant функциите, и някой го държи. Кой ли?

(gdb) p lock
$3 = {__data = {__lock = 2, __count = 0, __owner = 16609, __nusers = 1, __kind = 0, __spins = 0, __elision = 0, __list = {__prev = 0x0, __next = 0x0}},
__size = "\002\000\000\000\000\000\000\000\[email protected]\000\000\001", '\000' , __align = 2}
(gdb) p &lock
$4 = (__libc_lock_t *) 0x7fdbf908c2c0

Тук се вижда как owner-а на lock-а всъщност е parent-а. Той обаче не смята, че го държи:

(gdb) p lock
$2 = 0
(gdb) p &lock
$3 = (__libc_lock_t *) 0x7fdbf9450df0
(gdb) x/20x 0x7fdbf9450df0
: 0x00000000 0x00000000 0x00000000 0x00000000
0x7fdbf9450e00 <__abort_msg>: 0x00000000 0x00000000 0x00000000 0x00000000
0x7fdbf9450e10 : 0x00000000 0x00000000 0x00000000 0x00000000
0x7fdbf9450e20 : 0x00000000 0x00000000 0x00000000 0x00000000
0x7fdbf9450e30 : 0x001762c9 0x00000000 0x00000000 0x00000000

… което е и съвсем очаквано, при условие, че са два процеса и тая памет не е обща.

Та, явно това, което се е случило е, че докато parent-а е правел fork(), тоя lock го е държал някой, и child-а реално не може да пипне каквото и да е, свързано с него (което значи никакви reentrant функции в glibc-то, каквито па всички ползват (и би трябвало да ползват)). Въпросът е, че по принцип това не би трябвало да е възможно, щото около fork() няма нищо, което да взима тоя lock, и би трябвало glibc да си освобождава lock-а като излиза от функциите си.

Първоначалното ми идиотско предположение беше, че в signal handler-а на SIGCHLD multiprocessing модула създава новите child-ове, и така докато нещо друго държи lock-а идва сигнал, прави се нов процес и той го “наследява” заключен. Това беше твърде глупаво, за да е истина, и се оказа, че не е…

Около въпросите с lock-а бях стигнал с търсене до две неща – issue 127 в gperftools и Debian bug 657835. Първото каза, че проблемът ми може да е от друг lock, който някой друг държи преди fork-а (което ме накара да се загледам по-внимателно какви lock-ове се държат), а второто, че като цяло ако fork-ваш thread-нато приложение, може после единствено да правиш execve(), защото всичко друго не е ясно колко ще работи.

И накрая се оказа, че ако се ползва multiprocessing модула, той пуска в главния процес няколко thread-а, които да се занимават със следенето и пускането на child-ове за обработка. Та ето какво реално се случва:

– някой child си изработва нужния брой операции и излиза
– parent-а получава SIGCHLD и си отбелязва, че трябва да види какво става
– главния thread на parent-а тръгва да събира списъка бази, и вика в някакъв момент _nss_files_gethostbyname4_r, който взима lock-а;
– по това време другия thread казва “а, нямам достатъчно child-ове, fork()”
– profit.

Текущото ми глупаво решение е да не правя нищо в главния thread, което може да взима тоя lock и да се надявам, че няма още някой такъв. Бъдещото ми решение е или да го пиша на python3 с някой друг модул по темата, или на go (което ще трябва да науча).

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.

Fedora 28 released

Post Syndicated from corbet original https://lwn.net/Articles/753208/rss

The Fedora 28 release has been announced.
The headline feature for Fedora 28 Server is the inclusion of the
new Modular repository. This lets you select between different versions of
software like NodeJS or Django, so you can chose the stack you need for
your software.
” Some users will also appreciate that proprietary
blobs (such as the NVIDIA drivers) are now easier to obtain and install.

No, Ray Ozzie hasn’t solved crypto backdoors

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/04/no-ray-ozzie-hasnt-solved-crypto.html

According to this Wired article, Ray Ozzie may have a solution to the crypto backdoor problem. No, he hasn’t. He’s only solving the part we already know how to solve. He’s deliberately ignoring the stuff we don’t know how to solve. We know how to make backdoors, we just don’t know how to secure them.

The vault doesn’t scale

Yes, Apple has a vault where they’ve successfully protected important keys. No, it doesn’t mean this vault scales. The more people and the more often you have to touch the vault, the less secure it becomes. We are talking thousands of requests per day from 100,000 different law enforcement agencies around the world. We are unlikely to protect this against incompetence and mistakes. We are definitely unable to secure this against deliberate attack.

A good analogy to Ozzie’s solution is LetsEncrypt for getting SSL certificates for your website, which is fairly scalable, using a private key locked in a vault for signing hundreds of thousands of certificates. That this scales seems to validate Ozzie’s proposal.

But at the same time, LetsEncrypt is easily subverted. LetsEncrypt uses DNS to verify your identity. But spoofing DNS is easy, as was recently shown in the recent BGP attack against a cryptocurrency. Attackers can create fraudulent SSL certificates with enough effort. We’ve got other protections against this, such as discovering and revoking the SSL bad certificate, so while damaging, it’s not catastrophic.

But with Ozzie’s scheme, equivalent attacks would be catastrophic, as it would lead to unlocking the phone and stealing all of somebody’s secrets.

In particular, consider what would happen if LetsEncrypt’s certificate was stolen (as Matthew Green points out). The consequence is that this would be detected and mass revocations would occur. If Ozzie’s master key were stolen, nothing would happen. Nobody would know, and evildoers would be able to freely decrypt phones. Ozzie claims his scheme can work because SSL works — but then his scheme includes none of the many protections necessary to make SSL work.

What I’m trying to show here is that in a lab, it all looks nice and pretty, but when attacked at scale, things break down — quickly. We have so much experience with failure at scale that we can judge Ozzie’s scheme as woefully incomplete. It’s not even up to the standard of SSL, and we have a long list of SSL problems.

Cryptography is about people more than math

We have a mathematically pure encryption algorithm called the “One Time Pad”. It can’t ever be broken, provably so with mathematics.

It’s also perfectly useless, as it’s not something humans can use. That’s why we use AES, which is vastly less secure (anything you encrypt today can probably be decrypted in 100 years). AES can be used by humans whereas One Time Pads cannot be. (I learned the fallacy of One Time Pad’s on my grandfather’s knee — he was a WW II codebreaker who broke German messages trying to futz with One Time Pads).

The same is true with Ozzie’s scheme. It focuses on the mathematical model but ignores the human element. We already know how to solve the mathematical problem in a hundred different ways. The part we don’t know how to secure is the human element.

How do we know the law enforcement person is who they say they are? How do we know the “trusted Apple employee” can’t be bribed? How can the law enforcement agent communicate securely with the Apple employee?

You think these things are theoretical, but they aren’t. Consider financial transactions. It used to be common that you could just email your bank/broker to wire funds into an account for such things as buying a house. Hackers have subverted that, intercepting messages, changing account numbers, and stealing millions. Most banks/brokers require additional verification before doing such transfers.

Let me repeat: Ozzie has only solved the part we already know how to solve. He hasn’t addressed these issues that confound us.

We still can’t secure security, much less secure backdoors

We already know how to decrypt iPhones: just wait a year or two for somebody to discover a vulnerability. FBI claims it’s “going dark”, but that’s only for timely decryption of phones. If they are willing to wait a year or two a vulnerability will eventually be found that allows decryption.

That’s what’s happened with the “GrayKey” device that’s been all over the news lately. Apple is fixing it so that it won’t work on new phones, but it works on old phones.

Ozzie’s solution is based on the assumption that iPhones are already secure against things like GrayKey. Like his assumption “if Apple already has a vault for private keys, then we have such vaults for backdoor keys”, Ozzie is saying “if Apple already had secure hardware/software to secure the phone, then we can use the same stuff to secure the backdoors”. But we don’t really have secure vaults and we don’t really have secure hardware/software to secure the phone.

Again, to stress this point, Ozzie is solving the part we already know how to solve, but ignoring the stuff we don’t know how to solve. His solution is insecure for the same reason phones are already insecure.

Locked phones aren’t the problem

Phones are general purpose computers. That means anybody can install an encryption app on the phone regardless of whatever other security the phone might provide. The police are powerless to stop this. Even if they make such encryption crime, then criminals will still use encryption.

That leads to a strange situation that the only data the FBI will be able to decrypt is that of people who believe they are innocent. Those who know they are guilty will install encryption apps like Signal that have no backdoors.

In the past this was rare, as people found learning new apps a barrier. These days, apps like Signal are so easy even drug dealers can figure out how to use them.

We know how to get Apple to give us a backdoor, just pass a law forcing them to. It may look like Ozzie’s scheme, it may be something more secure designed by Apple’s engineers. Sure, it will weaken security on the phone for everyone, but those who truly care will just install Signal. But again we are back to the problem that Ozzie’s solving the problem we know how to solve while ignoring the much larger problem, that of preventing people from installing their own encryption.

The FBI isn’t necessarily the problem

Ozzie phrases his solution in terms of U.S. law enforcement. Well, what about Europe? What about Russia? What about China? What about North Korea?

Technology is borderless. A solution in the United States that allows “legitimate” law enforcement requests will inevitably be used by repressive states for what we believe would be “illegitimate” law enforcement requests.

Ozzie sees himself as the hero helping law enforcement protect 300 million American citizens. He doesn’t see himself what he really is, the villain helping oppress 1.4 billion Chinese, 144 million Russians, and another couple billion living in oppressive governments around the world.


Ozzie pretends the problem is political, that he’s created a solution that appeases both sides. He hasn’t. He’s solved the problem we already know how to solve. He’s ignored all the problems we struggle with, the problems we claim make secure backdoors essentially impossible. I’ve listed some in this post, but there are many more. Any famous person can create a solution that convinces fawning editors at Wired Magazine, but if Ozzie wants to move forward he’s going to have to work harder to appease doubting cryptographers.

Two NSA Algorithms Rejected by the ISO

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/two_nsa_algorit.html

The ISO has rejected two symmetric encryption algorithms: SIMON and SPECK. These algorithms were both designed by the NSA and made public in 2013. They are optimized for small and low-cost processors like IoT devices.

The risk of using NSA-designed ciphers, of course, is that they include NSA-designed backdoors. Personally, I doubt that they’re backdoored. And I always like seeing NSA-designed cryptography (particularly its key schedules). It’s like examining alien technology.

Security updates for Monday

Post Syndicated from ris original https://lwn.net/Articles/752544/rss

Security updates have been issued by Debian (gunicorn, libreoffice, libsdl2-image, ruby1.8, and ruby1.9.1), Fedora (java-1.8.0-openjdk, jgraphx, memcached, nghttp2, perl, perl-Module-CoreList, and roundcubemail), Gentoo (clamav, librelp, mbedtls, quagga, tenshi, and unadf), Mageia (freeplane, libcdio, libtiff, thunderbird, and zsh), openSUSE (cfitsio, chromium, mbedtls, and nextcloud), and Red Hat (chromium-browser, kernel, and rh-perl524-perl).