Tag Archives: tesla

You wouldn’t download a car…

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/you-wouldnt-download-car-telsa-hack/

You wouldn’t download a car…but is that just because none of us know how to? And OF COURSE none of us know how to: it’s a really hard thing to do!

Raspberry Pi Tesla

Dramatic reenactment using a Mini because, c’mon, as if I can afford a Tesla!

Nikola Tesla was in love with a pigeon 😍🐦

True story. He was also the true father of the electrical age (sorry, not sorry, Edison) and looked so much like David Bowie that here’s David Bowie playing Nikola Tesla:

David Bowie as Nicola Tesla — Raspberry Pi Tesla

Not even pigeon love

Which is the perfect segue, as here’s a Tesla playing David Bowie, and here’s also where our story truly begins…

Some people dislike Tesla (the car manufacturer, not the scientist) but we love them

But some people also dislike going to the dentist, so ¯\_(ツ)_/¯. (I also love going to the dentist.)

I’m pretty sure the reason some people have issues with Tesla is that electric cars still seem like a form of magic we’re not quite comfortable with.

Whatever people’s reason for holding a grudge against Tesla, recent findings at a university in Belgium this week have left the tech community aflutter: the academics announced that, with the aid of a “$35 computer”, they can clone your Tesla car key and steal. Your. Car.

If you haven’t guessed yet, we’re the ones behind the $35 computer. (Hi!)

Says WIRED: A team of researchers at the KU Leuven University in Belgium on Monday plan to present a paper at the Cryptographic Hardware and Embedded Systems conference in Amsterdam, revealing a technique for defeating the encryption used in the wireless key fobs of Tesla’s Model S luxury sedans. With about $600 in radio and computing equipment, they can wirelessly read signals from a nearby Tesla owner’s fob. Less than two seconds of computation yields the fob’s cryptographic key, allowing them to steal the associated car without a trace.

When I said that the tech community was all aflutter, what I meant was, on the whole, we find this hack somewhat entertaining but aren’t all that shocked by it. Not because we hate Tesla, but because these things happen. Technology is ever evolving, and that $600 worth of kit can do a thing to another thing isn’t all that unbelievable.

Sweet Cyber Jones on Twitter

The keys to my new Tesla https://t.co/jNViEZBxrB

The academics showed an example of the hack using “just” a couple of radios, a Raspberry Pi, some batteries, and your basic, off-the-shelf “pre-computed table of keys on a portable hard drive”. And through the magic of electric car IoT technology, Tesla instantly released a series of fixes to allow existing Tesla users to protect their cars against the attack, which is all kinds of cool.

Alex, why are you making such light of this?!

Because The Fast and the Furious isn’t real. And I highly doubt there’s a criminal enterprise out there that’s capable of building the same technology as well-funded university researchers.

Yes, this study from KU Leuven University is interesting. And yes, we all had a good laugh at the expense of Tesla and Elon Musk, but we don’t need academics to provide material for that. And I genuinely love Tesla and the work Elon is doing. True love.

Instead, we should be seeing this as a reminder that data encryption and online security are things we all need to take seriously in this digital world. So stop connecting your phone to whatever free WiFi network you can find, stop using PASSWORD123 for all your online accounts, and spend a little more time learning how you can better protect yourself and your family from nasty people on the internet.

And leave Britney Tesla alone!

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MagPi 70: Home automation with Raspberry Pi

Post Syndicated from Rob Zwetsloot original https://www.raspberrypi.org/blog/magpi-70-home-automation/

Hey folks, Rob here! It’s the last Thursday of the month, and that means it’s time for a brand-new The MagPi. Issue 70 is all about home automation using your favourite microcomputer, the Raspberry Pi.

Cover of The MagPi 70 — Raspberry Pi home automation and tech upcycling

Home automation in this month’s The MagPi!

Raspberry Pi home automation

We think home automation is an excellent use of the Raspberry Pi, hiding it around your house and letting it power your lights and doorbells and…fish tanks? We show you how to do all of that, and give you some excellent tips on how to add even more automation to your home in our ten-page cover feature.

Upcycle your life

Our other big feature this issue covers upcycling, the hot trend of taking old electronics and making them better than new with some custom code and a tactically placed Raspberry Pi. For this feature, we had a chat with Martin Mander, upcycler extraordinaire, to find out his top tips for hacking your old hardware.

Article on upcycling in The MagPi 70 — Raspberry Pi home automation and tech upcycling

Upcycling is a lot of fun

But wait, there’s more!

If for some reason you want even more content, you’re in luck! We have some fun tutorials for you to try, like creating a theremin and turning a Babbage into an IoT nanny cam. We also continue our quest to make a video game in C++. Our project showcase is headlined by the Teslonda on page 28, a Honda/Tesla car hybrid that is just wonderful.

Diddyborg V2 review in The MagPi 70 — Raspberry Pi home automation and tech upcycling

We review PiBorg’s latest robot

All this comes with our definitive reviews and the community section where we celebrate you, our amazing community! You’re all good beans

Teslonda article in The MagPi 70 — Raspberry Pi home automation and tech upcycling

An amazing, and practical, Raspberry Pi project

Get The MagPi 70

Issue 70 is available today from WHSmith, Tesco, Sainsbury’s, and Asda. If you live in the US, head over to your local Barnes & Noble or Micro Center in the next few days for a print copy. You can also get the new issue online from our store, or digitally via our Android and iOS apps. And don’t forget, there’s always the free PDF as well.

New subscription offer!

Want to support the Raspberry Pi Foundation and the magazine? We’ve launched a new way to subscribe to the print version of The MagPi: you can now take out a monthly £4 subscription to the magazine, effectively creating a rolling pre-order system that saves you money on each issue.

The MagPi subscription offer — Raspberry Pi home automation and tech upcycling

You can also take out a twelve-month print subscription and get a Pi Zero W plus case and adapter cables absolutely free! This offer does not currently have an end date.

That’s it for today! See you next month.

Animated GIF: a door slides open and Captain Picard emerges hesitantly

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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.

https://www.bloomberg.com/graphics/2018-tesla-tracker/

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:

The Software Freedom Conservancy on Tesla’s GPL compliance

Post Syndicated from corbet original https://lwn.net/Articles/754919/rss

The Software Freedom Conservancy has put out a
blog posting
on the history and current status of Tesla’s GPL
compliance issues. “We’re thus glad that, this week, Tesla has acted
publicly regarding its current GPL violations and has announced that
they’ve taken their first steps toward compliance. While Tesla acknowledges
that they still have more work to do, their recent actions show progress
toward compliance and a commitment to getting all the way there.

New – Amazon EC2 Instances with Up to 8 NVIDIA Tesla V100 GPUs (P3)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-amazon-ec2-instances-with-up-to-8-nvidia-tesla-v100-gpus-p3/

Driven by customer demand and made possible by on-going advances in the state-of-the-art, we’ve come a long way since the original m1.small instance that we launched in 2006, with instances that are emphasize compute power, burstable performance, memory size, local storage, and accelerated computing.

The New P3
Today we are making the next generation of GPU-powered EC2 instances available in four AWS regions. Powered by up to eight NVIDIA Tesla V100 GPUs, the P3 instances are designed to handle compute-intensive machine learning, deep learning, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, and genomics workloads.

P3 instances use customized Intel Xeon E5-2686v4 processors running at up to 2.7 GHz. They are available in three sizes (all VPC-only and EBS-only):

Model NVIDIA Tesla V100 GPUs GPU Memory NVIDIA NVLink vCPUs Main Memory Network Bandwidth EBS Bandwidth
p3.2xlarge 1 16 GiB n/a 8 61 GiB Up to 10 Gbps 1.5 Gbps
p3.8xlarge 4 64 GiB 200 GBps 32 244 GiB 10 Gbps 7 Gbps
p3.16xlarge 8 128 GiB 300 GBps 64 488 GiB 25 Gbps 14 Gbps

Each of the NVIDIA GPUs is packed with 5,120 CUDA cores and another 640 Tensor cores and can deliver up to 125 TFLOPS of mixed-precision floating point, 15.7 TFLOPS of single-precision floating point, and 7.8 TFLOPS of double-precision floating point. On the two larger sizes, the GPUs are connected together via NVIDIA NVLink 2.0 running at a total data rate of up to 300 GBps. This allows the GPUs to exchange intermediate results and other data at high speed, without having to move it through the CPU or the PCI-Express fabric.

What’s a Tensor Core?
I had not heard the term Tensor core before starting to write this post. According to this very helpful post on the NVIDIA Blog, Tensor cores are designed to speed up the training and inference of large, deep neural networks. Each core is able to quickly and efficiently multiply a pair of 4×4 half-precision (also known as FP16) matrices together, add the resulting 4×4 matrix to another half or single-precision (FP32) matrix, and store the resulting 4×4 matrix in either half or single-precision form. Here’s a diagram from NVIDIA’s blog post:

This operation is in the innermost loop of the training process for a deep neural network, and is an excellent example of how today’s NVIDIA GPU hardware is purpose-built to address a very specific market need. By the way, the mixed-precision qualifier on the Tensor core performance means that it is flexible enough to work with with a combination of 16-bit and 32-bit floating point values.

Performance in Perspective
I always like to put raw performance numbers into a real-world perspective so that they are easier to relate to and (hopefully) more meaningful. This turned out to be surprisingly difficult, given that the eight NVIDIA Tesla V100 GPUs on a single p3.16xlarge can do 125 trillion single-precision floating point multiplications per second.

Let’s go back to the dawn of the microprocessor era, and consider the Intel 8080A chip that powered the MITS Altair that I bought in the summer of 1977. With a 2 MHz clock, it was able to do about 832 multiplications per second (I used this data and corrected it for the faster clock speed). The p3.16xlarge is roughly 150 billion times faster. However, just 1.2 billion seconds have gone by since that summer. In other words, I can do 100x more calculations today in one second than my Altair could have done in the last 40 years!

What about the innovative 8087 math coprocessor that was an optional accessory for the IBM PC that was announced in the summer of 1981? With a 5 MHz clock and purpose-built hardware, it was able to do about 52,632 multiplications per second. 1.14 billion seconds have elapsed since then, p3.16xlarge is 2.37 billion times faster, so the poor little PC would be barely halfway through a calculation that would run for 1 second today.

Ok, how about a Cray-1? First delivered in 1976, this supercomputer was able to perform vector operations at 160 MFLOPS, making the p3.x16xlarge 781,000 times faster. It could have iterated on some interesting problem 1500 times over the years since it was introduced.

Comparisons between the P3 and today’s scale-out supercomputers are harder to make, given that you can think of the P3 as a step-and-repeat component of a supercomputer that you can launch on as as-needed basis.

Run One Today
In order to take full advantage of the NVIDIA Tesla V100 GPUs and the Tensor cores, you will need to use CUDA 9 and cuDNN7. These drivers and libraries have already been added to the newest versions of the Windows AMIs and will be included in an updated Amazon Linux AMI that is scheduled for release on November 7th. New packages are already available in our repos if you want to to install them on your existing Amazon Linux AMI.

The newest AWS Deep Learning AMIs come preinstalled with the latest releases of Apache MxNet, Caffe2, and Tensorflow (each with support for the NVIDIA Tesla V100 GPUs), and will be updated to support P3 instances with other machine learning frameworks such as Microsoft Cognitive Toolkit and PyTorch as soon as these frameworks release support for the NVIDIA Tesla V100 GPUs. You can also use the NVIDIA Volta Deep Learning AMI for NGC.

P3 instances are available in the US East (Northern Virginia), US West (Oregon), EU (Ireland), and Asia Pacific (Tokyo) Regions in On-Demand, Spot, Reserved Instance, and Dedicated Host form.

Jeff;

 

Derek Woodroffe’s steampunk tentacle hat

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/steampunk-tentacle-hat/

Halloween: that glorious time of year when you’re officially allowed to make your friends jump out of their skin with your pranks. For those among us who enjoy dressing up, Halloween is also the occasion to go all out with costumes. And so, dear reader, we present to you: a steampunk tentacle hat, created by Derek Woodroffe.

Finished Tenticle hat

Finished Tenticle hat

Extreme Electronics

Derek is an engineer who loves all things electronics. He’s part of Extreme Kits, and he runs the website Extreme Electronics. Raspberry Pi Zero-controlled Tesla coils are Derek’s speciality — he’s even been on one of the Royal Institution’s Christmas Lectures with them! Skip ahead to 15:06 in this video to see Derek in action:

Let There Be Light! // 2016 CHRISTMAS LECTURES with Saiful Islam – Lecture 1

The first Lecture from Professor Saiful Islam’s 2016 series of CHRISTMAS LECTURES, ‘Supercharged: Fuelling the future’. Watch all three Lectures here: http://richannel.org/christmas-lectures 2016 marked the 80th anniversary since the BBC first broadcast the Christmas Lectures on TV. To celebrate, chemist Professor Saiful Islam explores a subject that the lectures’ founder – Michael Faraday – addressed in the very first Christmas Lectures – energy.

Wearables

Wearables are electronically augmented items you can wear. They might take the form of spy eyeglasses, clothes with integrated sensors, or, in this case, headgear adorned with mechanised tentacles.

Why did Derek make this? We’re not entirely sure, but we suspect he’s a fan of the Cthulu mythos. In any case, we were a little astounded by his project. This is how we reacted when Derek tweeted us about it:

Raspberry Pi on Twitter

@ExtElec @extkits This is beyond incredible and completely unexpected.

In fact, we had to recover from a fit of laughter before we actually managed to type this answer.

Making a steampunk tentacle hat

Derek made the ‘skeleton’ of each tentacle out of a net curtain spring, acrylic rings, and four lengths of fishing line. Two servomotors connect to two ends of fishing line each, and pull them to move the tentacle.

net curtain spring and acrylic rings forming a mechanic tentacle skeleton - steampunk tentacle hat by Derek Woodroffe
Two servos connecting to lengths of fishing line - steampunk tentacle hat by Derek Woodroffe

Then he covered the tentacles with nylon stockings and liquid latex, glued suckers cut out of MDF onto them, and mounted them on an acrylic base. The eight motors connect to a Raspberry Pi via an I2C 8-port PWM controller board.

artificial tentacles - steampunk tentacle hat by Derek Woodroffe
8 servomotors connected to a controller board and a raspberry pi- steampunk tentacle hat by Derek Woodroffe

The Pi makes the servos pull the tentacles so that they move in sine waves in both the x and y directions, seemingly of their own accord. Derek cut open the top of a hat to insert the mounted tentacles, and he used more liquid latex to give the whole thing a slimy-looking finish.

steampunk tentacle hat by Derek Woodroffe

Iä! Iä! Cthulhu fhtagn!

You can read more about Derek’s steampunk tentacle hat here. He will be at the Beeston Raspberry Jam in November to show off his build, so if you’re in the Nottingham area, why not drop by?

Wearables for Halloween

This build is already pretty creepy, but just imagine it with a sensor- or camera-powered upgrade that makes the tentacles reach for people nearby. You’d have nightmare fodder for weeks.

With the help of the Raspberry Pi, any Halloween costume can be taken to the next level. How could Pi technology help you to win that coveted ‘Scariest costume’ prize this year? Tell us your ideas in the comments, and be sure to share pictures of you in your get-up with us on Twitter, Facebook, or Instagram.

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How to Compete with Giants

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/how-to-compete-with-giants/

How to Compete with Giants

This post by Backblaze’s CEO and co-founder Gleb Budman is the sixth in a series about entrepreneurship. You can choose posts in the series from the list below:

  1. How Backblaze got Started: The Problem, The Solution, and the Stuff In-Between
  2. Building a Competitive Moat: Turning Challenges Into Advantages
  3. From Idea to Launch: Getting Your First Customers
  4. How to Get Your First 1,000 Customers
  5. Surviving Your First Year
  6. How to Compete with Giants

Use the Join button above to receive notification of new posts in this series.

Perhaps your business is competing in a brand new space free from established competitors. Most of us, though, start companies that compete with existing offerings from large, established companies. You need to come up with a better mousetrap — not the first mousetrap.

That’s the challenge Backblaze faced. In this post, I’d like to share some of the lessons I learned from that experience.

Backblaze vs. Giants

Competing with established companies that are orders of magnitude larger can be daunting. How can you succeed?

I’ll set the stage by offering a few sets of giants we compete with:

  • When we started Backblaze, we offered online backup in a market where companies had been offering “online backup” for at least a decade, and even the newer entrants had raised tens of millions of dollars.
  • When we built our storage servers, the alternatives were EMC, NetApp, and Dell — each of which had a market cap of over $10 billion.
  • When we introduced our cloud storage offering, B2, our direct competitors were Amazon, Google, and Microsoft. You might have heard of them.

What did we learn by competing with these giants on a bootstrapped budget? Let’s take a look.

Determine What Success Means

For a long time Apple considered Apple TV to be a hobby, not a real product worth focusing on, because it did not generate a billion in revenue. For a $10 billion per year revenue company, a new business that generates $50 million won’t move the needle and often isn’t worth putting focus on. However, for a startup, getting to $50 million in revenue can be the start of a wildly successful business.

Lesson Learned: Don’t let the giants set your success metrics.

The Advantages Startups Have

The giants have a lot of advantages: more money, people, scale, resources, access, etc. Following their playbook and attacking head-on means you’re simply outgunned. Common paths to failure are trying to build more features, enter more markets, outspend on marketing, and other similar approaches where scale and resources are the primary determinants of success.

But being a startup affords many advantages most giants would salivate over. As a nimble startup you can leverage those to succeed. Let’s breakdown nine competitive advantages we’ve used that you can too.

1. Drive Focus

It’s hard to build a $10 billion revenue business doing just one thing, and most giants have a broad portfolio of businesses, numerous products for each, and targeting a variety of customer segments in multiple markets. That adds complexity and distributes management attention.

Startups get the benefit of having everyone in the company be extremely focused, often on a singular mission, product, customer segment, and market. While our competitors sell everything from advertising to Zantac, and are investing in groceries and shipping, Backblaze has focused exclusively on cloud storage. This means all of our best people (i.e. everyone) is focused on our cloud storage business. Where is all of your focus going?

Lesson Learned: Align everyone in your company to a singular focus to dramatically out-perform larger teams.

2. Use Lack-of-Scale as an Advantage

You may have heard Paul Graham say “Do things that don’t scale.” There are a host of things you can do specifically because you don’t have the same scale as the giants. Use that as an advantage.

When we look for data center space, we have more options than our largest competitors because there are simply more spaces available with room for 100 cabinets than for 1,000 cabinets. With some searching, we can find data center space that is better/cheaper.

When a flood in Thailand destroyed factories, causing the world’s supply of hard drives to plummet and prices to triple, we started drive farming. The giants certainly couldn’t. It was a bit crazy, but it let us keep prices unchanged for our customers.

Our Chief Cloud Officer, Tim, used to work at Adobe. Because of their size, any new product needed to always launch in a multitude of languages and in global markets. Once launched, they had scale. But getting any new product launched was incredibly challenging.

Lesson Learned: Use lack-of-scale to exploit opportunities that are closed to giants.

3. Build a Better Product

This one is probably obvious. If you’re going to provide the same product, at the same price, to the same customers — why do it? Remember that better does not always mean more features. Here’s one way we built a better product that didn’t require being a bigger company.

All online backup services required customers to choose what to include in their backup. We found that this was complicated for users since they often didn’t know what needed to be backed up. We flipped the model to back up everything and allow users to exclude if they wanted to, but it was not required. This reduced the number of features/options, while making it easier and better for the user.

This didn’t require the resources of a huge company; it just required understanding customers a bit deeper and thinking about the solution differently. Building a better product is the most classic startup competitive advantage.

Lesson Learned: Dig deep with your customers to understand and deliver a better mousetrap.

4. Provide Better Service

How can you provide better service? Use your advantages. Escalations from your customer care folks to engineering can go through fewer hoops. Fixing an issue and shipping can be quicker. Access to real answers on Twitter or Facebook can be more effective.

A strategic decision we made was to have all customer support people as full-time employees in our headquarters. This ensures they are in close contact to the whole company for feedback to quickly go both ways.

Having a smaller team and fewer layers enables faster internal communication, which increases customer happiness. And the option to do things that don’t scale — such as help a customer in a unique situation — can go a long way in building customer loyalty.

Lesson Learned: Service your customers better by establishing clear internal communications.

5. Remove The Unnecessary

After determining that the industry standard EMC/NetApp/Dell storage servers would be too expensive to build our own cloud storage upon, we decided to build our own infrastructure. Many said we were crazy to compete with these multi-billion dollar companies and that it would be impossible to build a lower cost storage server. However, not only did it prove to not be impossible — it wasn’t even that hard.

One key trick? Remove the unnecessary. While EMC and others built servers to sell to other companies for a wide variety of use cases, Backblaze needed servers that only Backblaze would run, and for a single use case. As a result we could tailor the servers for our needs by removing redundancy from each server (since we would run redundant servers), and using lower-performance components (since we would get high-performance by running parallel servers).

What do your customers and use cases not need? This can trim costs and complexity while often improving the product for your use case.

Lesson Learned: Don’t think “what can we add” to what the giants offer — think “what can we remove.”

6. Be Easy

How many times have you visited a large company website, particularly one that’s not consumer-focused, only to leave saying, “Huh? I don’t understand what you do.” Keeping your website clear, and your product and pricing simple, will dramatically increase conversion and customer satisfaction. If you’re able to make it 2x easier and thus increasing your conversion by 2x, you’ve just allowed yourself to spend ½ as much acquiring a customer.

Providing unlimited data backup wasn’t specifically about providing more storage — it was about making it easier. Since users didn’t know how much data they needed to back up, charging per gigabyte meant they wouldn’t know the cost. Providing unlimited data backup meant they could just relax.

Customers love easy — and being smaller makes easy easier to deliver. Use that as an advantage in your website, marketing materials, pricing, product, and in every other customer interaction.

Lesson Learned: Ease-of-use isn’t a slogan: it’s a competitive advantage. Treat it as seriously as any other feature of your product

7. Don’t Be Afraid of Risk

Obviously unnecessary risks are unnecessary, and some risks aren’t worth taking. However, large companies that have given guidance to Wall Street with a $0.01 range on their earning-per-share are inherently going to be very risk-averse. Use risk-tolerance to open up opportunities, and adjust your tolerance level as you scale. In your first year, there are likely an infinite number of ways your business may vaporize; don’t be too worried about taking a risk that might have a 20% downside when the upside is hockey stick growth.

Using consumer-grade hard drives in our servers may have caused pain and suffering for us years down-the-line, but they were priced at approximately 50% of enterprise drives. Giants wouldn’t have considered the option. Turns out, the consumer drives performed great for us.

Lesson Learned: Use calculated risks as an advantage.

8. Be Open

The larger a company grows, the more it wants to hide information. Some of this is driven by regulatory requirements as a public company. But most of this is cultural. Sharing something might cause a problem, so let’s not. All external communication is treated as a critical press release, with rounds and rounds of editing by multiple teams and approvals. However, customers are often desperate for information. Moreover, sharing information builds trust, understanding, and advocates.

I started blogging at Backblaze before we launched. When we blogged about our Storage Pod and open-sourced the design, many thought we were crazy to share this information. But it was transformative for us, establishing Backblaze as a tech thought leader in storage and giving people a sense of how we were able to provide our service at such a low cost.

Over the years we’ve developed a culture of being open internally and externally, on our blog and with the press, and in communities such as Hacker News and Reddit. Often we’ve been asked, “why would you share that!?” — but it’s the continual openness that builds trust. And that culture of openness is incredibly challenging for the giants.

Lesson Learned: Overshare to build trust and brand where giants won’t.

9. Be Human

As companies scale, typically a smaller percent of founders and executives interact with customers. The people who build the company become more hidden, the language feels “corporate,” and customers start to feel they’re interacting with the cliche “faceless, nameless corporation.” Use your humanity to your advantage. From day one the Backblaze About page listed all the founders, and my email address. While contacting us shouldn’t be the first path for a customer support question, I wanted it to be clear that we stand behind the service we offer; if we’re doing something wrong — I want to know it.

To scale it’s important to have processes and procedures, but sometimes a situation falls outside of a well-established process. While we want our employees to follow processes, they’re still encouraged to be human and “try to do the right thing.” How to you strike this balance? Simon Sinek gives a good talk about it: make your employees feel safe. If employees feel safe they’ll be human.

If your customer is a consumer, they’ll appreciate being treated as a human. Even if your customer is a corporation, the purchasing decision-makers are still people.

Lesson Learned: Being human is the ultimate antithesis to the faceless corporation.

Build Culture to Sustain Your Advantages at Scale

Presumably the goal is not to always be competing with giants, but to one day become a giant. Does this mean you’ll lose all of these advantages? Some, yes — but not all. Some of these advantages are cultural, and if you build these into the culture from the beginning, and fight to keep them as you scale, you can keep them as you become a giant.

Tesla still comes across as human, with Elon Musk frequently interacting with people on Twitter. Apple continues to provide great service through their Genius Bar. And, worst case, if you lose these at scale, you’ll still have the other advantages of being a giant such as money, people, scale, resources, and access.

Of course, some new startup will be gunning for you with grand ambitions, so just be sure not to get complacent. 😉

The post How to Compete with Giants appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Self-Driving Cars Should Be Open Source

Post Syndicated from Bozho original https://techblog.bozho.net/self-driving-cars-open-source/

Self-driving cars are (will be) the pinnacle of consumer products automation – robot vacuum cleaners, smart fridges and TVs are just toys compared to self-driving cars. Both in terms of technology and in terms of impact. We aren’t yet on level 5 self driving cars , but they are behind the corner.

But as software engineers we know how fragile software is. And self-driving cars are basically software, so we can see all the risks involved with putting our lives in the hands anonymous (from our point of view) developers and unknown (to us) processes and quality standards. One may argue that this has been the case for every consumer product ever, but with software is different – software is way more complex than anything else.

So I have an outrageous proposal – self-driving cars should be open source. We have to be able to verify and trust the code that’s navigating our helpless bodies around the highways. Not only that, but we have to be able to verify if it is indeed that code that is currently running in our car, and not something else.

In fact, let me extend that – all cars should be open source. Before you say “but that will ruin the competitive advantage of manufacturers and will be deadly for business”, I don’t actually care how they trained their neural networks, or what their datasets are. That’s actually the secret sauce of the self-driving car and in my view it can remain proprietary and closed. What I’d like to see open-sourced is everything else. (Under what license – I’d be fine to even have it copyrighted and so not “real” open source, but that’s a separate discussion).

Why? This story about remote carjacking using the entertainment system of a Jeep is a scary example. Attackers that reverse engineer the car software can remotely control everything in the car. Why did that happen? Well, I guess it’s complicated and we have to watch the DEFCON talk.

And also read the paper, but a paragraph in wikipedia about the CAN bus used in most cars gives us a hint:

CAN is a low-level protocol and does not support any security features intrinsically. There is also no encryption in standard CAN implementations, which leaves these networks open to man-in-the-middle packet interception. In most implementations, applications are expected to deploy their own security mechanisms; e.g., to authenticate incoming commands or the presence of certain devices on the network. Failure to implement adequate security measures may result in various sorts of attacks if the opponent manages to insert messages on the bus. While passwords exist for some safety-critical functions, such as modifying firmware, programming keys, or controlling antilock brake actuators, these systems are not implemented universally and have a limited number of seed/key pair

I don’t know in what world it makes sense to even have a link between the entertainment system and the low-level network that operates the physical controls. As apparent from the talk, the two systems are supposed to be air-gapped, but in reality they aren’t.

Rookie mistakes were abound – unauthenticated “execute” method, running as root, firmware is not signed, hard-coded passwords, etc. How do we know that there aren’t tons of those in all cars out there right now, and in the self-driving cars of the future (which will likely use the same legacy technologies of the current cars)? Recently I heard a negative comment about the source code of one of the self-driving cars “players”, and I’m pretty sure there are many of those rookie mistakes.

Why this is this even more risky for self-driving cars? I’m not an expert in car programming, but it seems like the attack surface is bigger. I might be completely off target here, but on a typical car you’d have to “just” properly isolate the CAN bus. With self-driving cars the autonomous system that watches the surrounding and makes decisions on what to do next has to be connected to the CAN bus. With Tesla being able to send updates over the wire, the attack surface is even bigger (although that’s actually a good feature – to be able to patch all cars immediately once a vulnerability is discovered).

Of course, one approach would be to introduce legislation that regulates car software. It might work, but it would rely on governments to to proper testing, which won’t always be the case.

The alternative is to open-source it and let all the white-hats find your issues, so that you can close them before the car hits the road. Not only that, but consumers like me will feel safer, and geeks would be able to verify whether the car is really running the software it claims to run by verifying the fingerprints.

Richard Stallman might be seen as a fanatic when he advocates against closed source software, but in cases like … cars, his concerns seem less extreme.

“But the Jeep vulnerability was fixed”, you may say. And that might be seen as being the way things are – vulnerabilities appear, they get fixed, life goes on. No person was injured because of the bug, right? Well, not yet. And “gaining control” is the extreme scenario – there are still pretty bad scenarios, like being able to track a car through its GPS, or cause panic by controlling the entertainment system. It might be over wifi, or over GPRS, or even by physically messing with the car by inserting a flash drive. Is open source immune to those issues? No, but it has proven to be more resilient.

One industry where the problem of proprietary software on a product that the customer bought is … tractors. It turns out farmers are hacking their tractors, because of multiple issues and the inability of the vendor to resolve them in a timely manner. This is likely to happen to cars soon, when only authorized repair shops are allowed to touch anything on the car. And with unauthorized repair shops the attack surface becomes even bigger.

In fact, I’d prefer open source not just for cars, but for all consumer products. The source code of a smart fridge or a security camera is trivial, it would rarely mean sacrificing competitive advantage. But refrigerators get hacked, security cameras are active part of botnets, the “internet of shit” is getting ubiquitous. A huge amount of these issues are dumb, beginner mistakes. We have the right to know what shit we are running – in our frdges, DVRs and ultimatey – cars.

Your fridge may soon by spying on you, your vacuum cleaner may threaten your pet in demand of “ransom”. The terrorists of the future may crash planes without being armed, can crash vans into crowds without being in the van, and can “explode” home equipment without being in the particular home. And that’s not just a hypothetical.

Will open source magically solve the issue? No. But it will definitely make things better and safer, as it has done with operating systems and web servers.

The post Self-Driving Cars Should Be Open Source appeared first on Bozho's tech blog.

Delivering Graphics Apps with Amazon AppStream 2.0

Post Syndicated from Deepak Suryanarayanan original https://aws.amazon.com/blogs/compute/delivering-graphics-apps-with-amazon-appstream-2-0/

Sahil Bahri, Sr. Product Manager, Amazon AppStream 2.0

Do you need to provide a workstation class experience for users who run graphics apps? With Amazon AppStream 2.0, you can stream graphics apps from AWS to a web browser running on any supported device. AppStream 2.0 offers a choice of GPU instance types. The range includes the newly launched Graphics Design instance, which allows you to offer a fast, fluid user experience at a fraction of the cost of using a graphics workstation, without upfront investments or long-term commitments.

In this post, I discuss the Graphics Design instance type in detail, and how you can use it to deliver a graphics application such as Siemens NX―a popular CAD/CAM application that we have been testing on AppStream 2.0 with engineers from Siemens PLM.

Graphics Instance Types on AppStream 2.0

First, a quick recap on the GPU instance types available with AppStream 2.0. In July, 2017, we launched graphics support for AppStream 2.0 with two new instance types that Jeff Barr discussed on the AWS Blog:

  • Graphics Desktop
  • Graphics Pro

Many customers in industries such as engineering, media, entertainment, and oil and gas are using these instances to deliver high-performance graphics applications to their users. These instance types are based on dedicated NVIDIA GPUs and can run the most demanding graphics applications, including those that rely on CUDA graphics API libraries.

Last week, we added a new lower-cost instance type: Graphics Design. This instance type is a great fit for engineers, 3D modelers, and designers who use graphics applications that rely on the hardware acceleration of DirectX, OpenGL, or OpenCL APIs, such as Siemens NX, Autodesk AutoCAD, or Adobe Photoshop. The Graphics Design instance is based on AMD’s FirePro S7150x2 Server GPUs and equipped with AMD Multiuser GPU technology. The instance type uses virtualized GPUs to achieve lower costs, and is available in four instance sizes to scale and match the requirements of your applications.

Instance vCPUs Instance RAM (GiB) GPU Memory (GiB)
stream.graphics-design.large 2 7.5 GiB 1
stream.graphics-design.xlarge 4 15.3 GiB 2
stream.graphics-design.2xlarge 8 30.5 GiB 4
stream.graphics-design.4xlarge 16 61 GiB 8

The following table compares all three graphics instance types on AppStream 2.0, along with example applications you could use with each.

  Graphics Design Graphics Desktop Graphics Pro
Number of instance sizes 4 1 3
GPU memory range
1–8 GiB 4 GiB 8–32 GiB
vCPU range 2–16 8 16–32
Memory range 7.5–61 GiB 15 GiB 122–488 GiB
Graphics libraries supported AMD FirePro S7150x2 NVIDIA GRID K520 NVIDIA Tesla M60
Price range (N. Virginia AWS Region) $0.25 – $2.00/hour $0.5/hour $2.05 – $8.20/hour
Example applications Adobe Premiere Pro, AutoDesk Revit, Siemens NX AVEVA E3D, SOLIDWORKS AutoDesk Maya, Landmark DecisionSpace, Schlumberger Petrel

Example graphics instance set up with Siemens NX

In the section, I walk through setting up Siemens NX with Graphics Design instances on AppStream 2.0. After set up is complete, users can able to access NX from within their browser and also access their design files from a file share. You can also use these steps to set up and test your own graphics applications on AppStream 2.0. Here’s the workflow:

  1. Create a file share to load and save design files.
  2. Create an AppStream 2.0 image with Siemens NX installed.
  3. Create an AppStream 2.0 fleet and stack.
  4. Invite users to access Siemens NX through a browser.
  5. Validate the setup.

To learn more about AppStream 2.0 concepts and set up, see the previous post Scaling Your Desktop Application Streams with Amazon AppStream 2.0. For a deeper review of all the setup and maintenance steps, see Amazon AppStream 2.0 Developer Guide.

Step 1: Create a file share to load and save design files

To launch and configure the file server

  1. Open the EC2 console and choose Launch Instance.
  2. Scroll to the Microsoft Windows Server 2016 Base Image and choose Select.
  3. Choose an instance type and size for your file server (I chose the general purpose m4.large instance). Choose Next: Configure Instance Details.
  4. Select a VPC and subnet. You launch AppStream 2.0 resources in the same VPC. Choose Next: Add Storage.
  5. If necessary, adjust the size of your EBS volume. Choose Review and Launch, Launch.
  6. On the Instances page, give your file server a name, such as My File Server.
  7. Ensure that the security group associated with the file server instance allows for incoming traffic from the security group that you select for your AppStream 2.0 fleets or image builders. You can use the default security group and select the same group while creating the image builder and fleet in later steps.

Log in to the file server using a remote access client such as Microsoft Remote Desktop. For more information about connecting to an EC2 Windows instance, see Connect to Your Windows Instance.

To enable file sharing

  1. Create a new folder (such as C:\My Graphics Files) and upload the shared files to make available to your users.
  2. From the Windows control panel, enable network discovery.
  3. Choose Server Manager, File and Storage Services, Volumes.
  4. Scroll to Shares and choose Start the Add Roles and Features Wizard. Go through the wizard to install the File Server and Share role.
  5. From the left navigation menu, choose Shares.
  6. Choose Start the New Share Wizard to set up your folder as a file share.
  7. Open the context (right-click) menu on the share and choose Properties, Permissions, Customize Permissions.
  8. Choose Permissions, Add. Add Read and Execute permissions for everyone on the network.

Step 2:  Create an AppStream 2.0 image with Siemens NX installed

To connect to the image builder and install applications

  1. Open the AppStream 2.0 management console and choose Images, Image Builder, Launch Image Builder.
  2. Create a graphics design image builder in the same VPC as your file server.
  3. From the Image builder tab, select your image builder and choose Connect. This opens a new browser tab and display a desktop to log in to.
  4. Log in to your image builder as ImageBuilderAdmin.
  5. Launch the Image Assistant.
  6. Download and install Siemens NX and other applications on the image builder. I added Blender and Firefox, but you could replace these with your own applications.
  7. To verify the user experience, you can test the application performance on the instance.

Before you finish creating the image, you must mount the file share by enabling a few Microsoft Windows services.

To mount the file share

  1. Open services.msc and check the following services:
  • DNS Client
  • Function Discovery Resource Publication
  • SSDP Discovery
  • UPnP Device H
  1. If any of the preceding services have Startup Type set to Manual, open the context (right-click) menu on the service and choose Start. Otherwise, open the context (right-click) menu on the service and choose Properties. For Startup Type, choose Manual, Apply. To start the service, choose Start.
  2. From the Windows control panel, enable network discovery.
  3. Create a batch script that mounts a file share from the storage server set up earlier. The file share is mounted automatically when a user connects to the AppStream 2.0 environment.

Logon Script Location: C:\Users\Public\logon.bat

Script Contents:

:loop

net use H: \\path\to\network\share 

PING localhost -n 30 >NUL

IF NOT EXIST H:\ GOTO loop

  1. Open gpedit.msc and choose User Configuration, Windows Settings, Scripts. Set logon.bat as the user logon script.
  2. Next, create a batch script that makes the mounted drive visible to the user.

Logon Script Location: C:\Users\Public\startup.bat

Script Contents:
REG DELETE “HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Policies\Explorer” /v “NoDrives” /f

  1. Open Task Scheduler and choose Create Task.
  2. Choose General, provide a task name, and then choose Change User or Group.
  3. For Enter the object name to select, enter SYSTEM and choose Check Names, OK.
  4. Choose Triggers, New. For Begin the task, choose At startup. Under Advanced Settings, change Delay task for to 5 minutes. Choose OK.
  5. Choose Actions, New. Under Settings, for Program/script, enter C:\Users\Public\startup.bat. Choose OK.
  6. Choose Conditions. Under Power, clear the Start the task only if the computer is on AC power Choose OK.
  7. To view your scheduled task, choose Task Scheduler Library. Close Task Scheduler when you are done.

Step 3:  Create an AppStream 2.0 fleet and stack

To create a fleet and stack

  1. In the AppStream 2.0 management console, choose Fleets, Create Fleet.
  2. Give the fleet a name, such as Graphics-Demo-Fleet, that uses the newly created image and the same VPC as your file server.
  3. Choose Stacks, Create Stack. Give the stack a name, such as Graphics-Demo-Stack.
  4. After the stack is created, select it and choose Actions, Associate Fleet. Associate the stack with the fleet you created in step 1.

Step 4:  Invite users to access Siemens NX through a browser

To invite users

  1. Choose User Pools, Create User to create users.
  2. Enter a name and email address for each user.
  3. Select the users just created, and choose Actions, Assign Stack to provide access to the stack created in step 2. You can also provide access using SAML 2.0 and connect to your Active Directory if necessary. For more information, see the Enabling Identity Federation with AD FS 3.0 and Amazon AppStream 2.0 post.

Your user receives an email invitation to set up an account and use a web portal to access the applications that you have included in your stack.

Step 5:  Validate the setup

Time for a test drive with Siemens NX on AppStream 2.0!

  1. Open the link for the AppStream 2.0 web portal shared through the email invitation. The web portal opens in your default browser. You must sign in with the temporary password and set a new password. After that, you get taken to your app catalog.
  2. Launch Siemens NX and interact with it using the demo files available in the shared storage folder – My Graphics Files. 

After I launched NX, I captured the screenshot below. The Siemens PLM team also recorded a video with NX running on AppStream 2.0.

Summary

In this post, I discussed the GPU instances available for delivering rich graphics applications to users in a web browser. While I demonstrated a simple setup, you can scale this out to launch a production environment with users signing in using Active Directory credentials,  accessing persistent storage with Amazon S3, and using other commonly requested features reviewed in the Amazon AppStream 2.0 Launch Recap – Domain Join, Simple Network Setup, and Lots More post.

To learn more about AppStream 2.0 and capabilities added this year, see Amazon AppStream 2.0 Resources.

New – Next-Generation GPU-Powered EC2 Instances (G3)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-next-generation-gpu-powered-ec2-instances-g3/

I first wrote about the benefits of GPU-powered computing in 2013 when we launched the G2 instance type. Since that launch, AWS customers have used the G2 instances to deliver high performance graphics to mobile devices, TV sets, and desktops.

Today we are taking a step forward and launching the G3 instance type. Powered by NVIDIA Tesla M60 GPUs, these instances are available in three sizes (all VPC-only and EBS-only):

Model GPUs GPU Memory vCPUs Main Memory EBS Bandwidth
g3.4xlarge 1 8 GiB 16 122 GiB 3.5 Gbps
g3.8xlarge 2 16 GiB 32 244 GiB 7 Gbps
g3.16xlarge 4 32 GiB 64 488 GiB 14 Gbps

Each GPU supports 8 GiB of GPU memory, 2048 parallel processing cores, and a hardware encoder capable of supporting up to 10 H.265 (HEVC) 1080p30 streams and up to 18 H.264 1080p30 streams, making them a great fit for 3D rendering & visualization, virtual reality, video encoding, remote graphics workstation (NVIDIA GRID), and other server-side graphics workloads that need a massive amount of parallel processing power. The GPUs support OpenGL 4.5, DirectX 12.0, CUDA 8.0, and OpenCL 1.2. When you launch a G3 instance you have access to an NVIDIA GRID Virtual Workstation License and can make use of the NVIDIA GRID driver without purchasing a license on your own.

The instances use Intel Xeon E5-2686 v4 (Broadwell) processors running at 2.7 GHz. On the networking side, Enhanced Networking (via the Elastic Network Adapter) provides up to 20 Gbps of aggregate network bandwidth within a Placement Group, along with up to 14 Gbps of EBS bandwidth.

Our customers have told us that they are looking forward to visualizing large 3D seismic models, configuring cars in 3D, and providing students with the ability to run high-end 2D and 3D applications. For example, Calgary Scientific can take applications that are powered by the Unreal Engine and make them accessible on mobile devices and from within web pages, with collaborative viewing support. Visit their Demo Gallery to see PureWeb Reality in action:

You can launch these instances today in the US East (Ohio), US East (Northern Virginia), US West (Oregon), US West (Northern California), AWS GovCloud (US), and EU (Ireland) Regions as On-Demand, Reserved Instances, Spot Instances, and Dedicated Hosts, with more Regions coming soon.

Jeff;

Some notes on Trump’s cybersecurity Executive Order

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/05/some-notes-on-trumps-cybersecurity.html

President Trump has finally signed an executive order on “cybersecurity”. The first draft during his first weeks in power were hilariously ignorant. The current draft, though, is pretty reasonable as such things go. I’m just reading the plain language of the draft as a cybersecurity expert, picking out the bits that interest me. In reality, there’s probably all sorts of politics in the background that I’m missing, so I may be wildly off-base.

Holding managers accountable

This is a great idea in theory. But government heads are rarely accountable for anything, so it’s hard to see if they’ll have the nerve to implement this in practice. When the next breech happens, we’ll see if anybody gets fired.
“antiquated and difficult to defend Information Technology”

The government uses laughably old computers sometimes. Forces in government wants to upgrade them. This won’t work. Instead of replacing old computers, the budget will simply be used to add new computers. The old computers will still stick around.
“Legacy” is a problem that money can’t solve. Programmers know how to build small things, but not big things. Everything starts out small, then becomes big gradually over time through constant small additions. What you have now is big legacy systems. Attempts to replace a big system with a built-from-scratch big system will fail, because engineers don’t know how to build big systems. This will suck down any amount of budget you have with failed multi-million dollar projects.
It’s not the antiquated systems that are usually the problem, but more modern systems. Antiquated systems can usually be protected by simply sticking a firewall or proxy in front of them.

“address immediate unmet budgetary needs necessary to manage risk”

Nobody cares about cybersecurity. Instead, it’s a thing people exploit in order to increase their budget. Instead of doing the best security with the budget they have, they insist they can’t secure the network without more money.

An alternate way to address gaps in cybersecurity is instead to do less. Reduce exposure to the web, provide fewer services, reduce functionality of desktop computers, and so on. Insisting that more money is the only way to address unmet needs is the strategy of the incompetent.

Use the NIST framework
Probably the biggest thing in the EO is that it forces everyone to use the NIST cybersecurity framework.
The NIST Framework simply documents all the things that organizations commonly do to secure themselves, such run intrusion-detection systems or impose rules for good passwords.
There are two problems with the NIST Framework. The first is that no organization does all the things listed. The second is that many organizations don’t do the things well.
Password rules are a good example. Organizations typically had bad rules, such as frequent changes and complexity standards. So the NIST Framework documented them. But cybersecurity experts have long opposed those complex rules, so have been fighting NIST on them.

Another good example is intrusion-detection. These days, I scan the entire Internet, setting off everyone’s intrusion-detection systems. I can see first hand that they are doing intrusion-detection wrong. But the NIST Framework recommends they do it, because many organizations do it, but the NIST Framework doesn’t demand they do it well.
When this EO forces everyone to follow the NIST Framework, then, it’s likely just going to increase the amount of money spent on cybersecurity without increasing effectiveness. That’s not necessarily a bad thing: while probably ineffective or counterproductive in the short run, there might be long-term benefit aligning everyone to thinking about the problem the same way.
Note that “following” the NIST Framework doesn’t mean “doing” everything. Instead, it means documented how you do everything, a reason why you aren’t doing anything, or (most often) your plan to eventually do the thing.
preference for shared IT services for email, cloud, and cybersecurity
Different departments are hostile toward each other, with each doing things their own way. Obviously, the thinking goes, that if more departments shared resources, they could cut costs with economies of scale. Also obviously, it’ll stop the many home-grown wrong solutions that individual departments come up with.
In other words, there should be a single government GMail-type service that does e-mail both securely and reliably.
But it won’t turn out this way. Government does not have “economies of scale” but “incompetence at scale”. It means a single GMail-like service that is expensive, unreliable, and in the end, probably insecure. It means we can look forward to government breaches that instead of affecting one department affecting all departments.

Yes, you can point to individual organizations that do things poorly, but what you are ignoring is the organizations that do it well. When you make them all share a solution, it’s going to be the average of all these things — meaning those who do something well are going to move to a worse solution.

I suppose this was inserted in there so that big government cybersecurity companies can now walk into agencies, point to where they are deficient on the NIST Framework, and say “sign here to do this with our shared cybersecurity service”.
“identify authorities and capabilities that agencies could employ to support the cybersecurity efforts of critical infrastructure entities”
What this means is “how can we help secure the power grid?”.
What it means in practice is that fiasco in the Vermont power grid. The DHS produced a report containing IoCs (“indicators of compromise”) of Russian hackers in the DNC hack. Among the things it identified was that the hackers used Yahoo! email. They pushed these IoCs out as signatures in their “Einstein” intrusion-detection system located at many power grid locations. The next person that logged into their Yahoo! email was then flagged as a Russian hacker, causing all sorts of hilarity to ensue, such as still uncorrected stories by the Washington Post how the Russians hacked our power-grid.
The upshot is that federal government help is also going to include much government hindrance. They really are this stupid sometimes and there is no way to fix this stupid. (Seriously, the DHS still insists it did the right thing pushing out the Yahoo IoCs).
Resilience Against Botnets and Other Automated, Distributed Threats

The government wants to address botnets because it’s just the sort of problem they love, mass outages across the entire Internet caused by a million machines.

But frankly, botnets don’t even make the top 10 list of problems they should be addressing. Number #1 is clearly “phishing” — you know, the attack that’s been getting into the DNC and Podesta e-mails, influencing the election. You know, the attack that Gizmodo recently showed the Trump administration is partially vulnerable to. You know, the attack that most people blame as what probably led to that huge OPM hack. Replace the entire Executive Order with “stop phishing”, and you’d go further fixing federal government security.

But solving phishing is tough. To begin with, it requires a rethink how the government does email, and how how desktop systems should be managed. So the government avoids complex problems it can’t understand to focus on the simple things it can — botnets.

Dealing with “prolonged power outage associated with a significant cyber incident”

The government has had the hots for this since 2001, even though there’s really been no attack on the American grid. After the Russian attacks against the Ukraine power grid, the issue is heating up.

Nation-wide attacks aren’t really a threat, yet, in America. We have 10,000 different companies involved with different systems throughout the country. Trying to hack them all at once is unlikely. What’s funny is that it’s the government’s attempts to standardize everything that’s likely to be our downfall, such as sticking Einstein sensors everywhere.

What they should be doing is instead of trying to make the grid unhackable, they should be trying to lessen the reliance upon the grid. They should be encouraging things like Tesla PowerWalls, solar panels on roofs, backup generators, and so on. Indeed, rather than industrial system blackout, industry backup power generation should be considered as a source of grid backup. Factories and even ships were used to supplant the electric power grid in Japan after the 2011 tsunami, for example. The less we rely on the grid, the less a blackout will hurt us.

“cybersecurity risks facing the defense industrial base, including its supply chain”

So “supply chain” cybersecurity is increasingly becoming a thing. Almost anything electronic comes with millions of lines of code, silicon chips, and other things that affect the security of the system. In this context, they may be worried about intentional subversion of systems, such as that recent article worried about Kaspersky anti-virus in government systems. However, the bigger concern is the zillions of accidental vulnerabilities waiting to be discovered. It’s impractical for a vendor to secure a product, because it’s built from so many components the vendor doesn’t understand.

“strategic options for deterring adversaries and better protecting the American people from cyber threats”

Deterrence is a funny word.

Rumor has it that we forced China to backoff on hacking by impressing them with our own hacking ability, such as reaching into China and blowing stuff up. This works because the Chinese governments remains in power because things are going well in China. If there’s a hiccup in economic growth, there will be mass actions against the government.

But for our other cyber adversaries (Russian, Iran, North Korea), things already suck in their countries. It’s hard to see how we can make things worse by hacking them. They also have a strangle hold on the media, so hacking in and publicizing their leader’s weird sex fetishes and offshore accounts isn’t going to work either.

Also, deterrence relies upon “attribution”, which is hard. While news stories claim last year’s expulsion of Russian diplomats was due to election hacking, that wasn’t the stated reason. Instead, the claimed reason was Russia’s interference with diplomats in Europe, such as breaking into diplomat’s homes and pooping on their dining room table. We know it’s them when they are brazen (as was the case with Chinese hacking), but other hacks are harder to attribute.

Deterrence of nation states ignores the reality that much of the hacking against our government comes from non-state actors. It’s not clear how much of all this Russian hacking is actually directed by the government. Deterrence polices may be better directed at individuals, such as the recent arrest of a Russian hacker while they were traveling in Spain. We can’t get Russian or Chinese hackers in their own countries, so we have to wait until they leave.

Anyway, “deterrence” is one of those real-world concepts that hard to shoe-horn into a cyber (“cyber-deterrence”) equivalent. It encourages lots of bad thinking, such as export controls on “cyber-weapons” to deter foreign countries from using them.

“educate and train the American cybersecurity workforce of the future”

The problem isn’t that we lack CISSPs. Such blanket certifications devalue the technical expertise of the real experts. The solution is to empower the technical experts we already have.

In other words, mandate that whoever is the “cyberczar” is a technical expert, like how the Surgeon General must be a medical expert, or how an economic adviser must be an economic expert. For over 15 years, we’ve had a parade of non-technical people named “cyberczar” who haven’t been experts.

Once you tell people technical expertise is valued, then by nature more students will become technical experts.

BTW, the best technical experts are software engineers and sysadmins. The best cybersecurity for Windows is already built into Windows, whose sysadmins need to be empowered to use those solutions. Instead, they are often overridden by a clueless cybersecurity consultant who insists on making the organization buy a third-party product instead that does a poorer job. We need more technical expertise in our organizations, sure, but not necessarily more cybersecurity professionals.

Conclusion

This is really a government document, and government people will be able to explain it better than I. These are just how I see it as a technical-expert who is a government-outsider.

My guess is the most lasting consequential thing will be making everyone following the NIST Framework, and the rest will just be a lot of aspirational stuff that’ll be ignored.

"Fast and Furious 8: Fate of the Furious"

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/04/fast-and-furious-8-fate-of-furious.html

So “Fast and Furious 8” opened this weekend to world-wide box office totals of $500,000,000. I thought I’d write up some notes on the “hacking” in it. The tl;dr version is this: yes, while the hacking is a bit far fetched, it’s actually more realistic than the car chase scenes, such as winning a race with the engine on fire while in reverse.

[SPOILERS]


Car hacking


The most innovative cyber-thing in the movie is the car hacking. In one scene, the hacker takes control of the cars in a parking structure, and makes them rain on to the street. In another scene, the hacker takes control away from drivers, with some jumping out of their moving cars in fear.

How real is this?

Well, today, few cars have a mechanical link between the computer and the steering wheel. No amount of hacking will fix the fact that this component is missing.

With that said, most new cars have features that make hacking possible. I’m not sure, but I’d guess more than half of new cars have internet connections (via the mobile phone network), cameras (for backing up, but also looking forward for lane departure warnings), braking (for emergencies), and acceleration.

In other words, we are getting really close.

As this Wikipedia article describes, there are levels for autonomous cars. At level 2 or 3, cars get automated steering, either for parking or for staying in the lane. Level 3 autonomy is especially useful, as it means you can sit back and relax while your car is sitting in a traffic jam. Higher levels of autonomy are still decades away, but most new cars, even the cheapest low end cars, will be level 3 within 5 years. That they make traffic jams bearable makes this an incredibly attractive feature.

Thus, while this scene is laughable today, it’ll be taken seriously in 10 years. People will look back on how smart this movie was at predicting the future.

Car hacking, part 2

Quite apart from the abilities of cars, let’s talk about the abilities of hackers.

The recent ShadowBrokers dump of NSA hacking tools show that hackers simply don’t have a lot of range. Hacking one car is easy — hacking all different models, makes, and years of cars is far beyond the ability of any hacking group, even the NSA.

I mean, a single hack may span more than one car model, and even across more than one manufacturer, because they buy such components from third-party manufacturers. Most cars that have cameras buy them from MobileEye, which was recently acquired by Intel.  As I blogged before, both my Parrot drone and Tesla car have the same WiFi stack, and both could be potential hacked with the same vulnerability. So hacking many cars at once isn’t totally out of the question.

It’s just that hacking all the different cars in a garage is completely implausible.

God’s Eye

The plot of the last two movies as been about the “God’s Eye”, a device that hacks into every camera and satellite to view everything going on in the world.

First of all, all hacking is software. The idea of stealing a hardware device in order enable hacking is therefore (almost) always fiction. There’s one corner case where a quantum chip factoring RSA would enable some previously impossible hacking, but it still can’t reach out and hack a camera behind a firewall.

Hacking security cameras around the world is indeed possible, though. The Mirai botnet of last year demonstrated this. It wormed its way form camera to camera, hacking hundreds of thousands of cameras that weren’t protected by firewalls. It used these devices as simply computers, to flood major websites, taking them offline. But it could’ve also used the camera features, to upload pictures and video’s to the hacker controlling these cameras.

However, most security cameras are behind firewalls, and can’t be reached. Building a “Gody’s Eye” view of the world, to catch a target every time they passed in front of a camera, would therefore be unrealistic.

Moreover, they don’t have either the processing power nor the bandwidth to work like that. It takes heavy number crunching in order to detect faces, or even simple things like license plates, within videos. The cameras don’t have that. Instead, cameras could upload the videos/pictures to supercomputers controlled by the hypothetical hacker, but the bandwidth doesn’t exist. The Internet is being rapidly upgraded, but still, Internet links are built for low-bandwidth webpages, not high-bandwidth streaming from millions of sources.

This rapidly changing. Cameras are rapidly being upgraded with “neural network” chips that will have some rudimentary capabilities to recognize things like license plates, or the outline of a face that could then be uploaded for more powerful number crunching elsewhere. Your car’s cameras already have this, for backup warnings and lane departure warnings, soon all security cameras will have something like this. Likewise, the Internet is steadily being upgraded to replace TV broadcast, where everyone can stream from Netflix all the time, so high-bandwidth streams from cameras will become more of the norm.

Even getting behind a firewall to the camera will change in the future, as owners will simply store surveillance video in the cloud instead of locally. Thus, the hypothetical hacker would only need to hack a small number of surveillance camera companies instead of a billion security cameras.

Evil villain lair: ghost airplane

The evil villain in the movie (named “Cipher”, or course) has her secret headquarters on an airplane that flies along satellite “blind spots” so that it can’t be tracked.

This is nonsense. Low resolution satellites, like NOAA satellites tracking the weather, cover the entire planet (well, as far as such airplanes are concerned, unless you are landing in Antartica). While such satellites might not see the plane, they can track the contrail (I mean, chemtrail). Conversely high resolution satellites miss most of the planet. If they haven’t been tasked to aim at something, they won’t see it. And they can’t be aimed at you unless they already know where you are. Sure, there are moving blind spots where even tasked satellites can’t find you, but it’s unlikely they’d be tracking you anyway.

Since the supervillain was a hacker, the airplane was full of computers. This is nonsense. Any compute power I need as a hacker is better left on the Earth’s surface, either by hacking cloud providers (like Amazon AWS, Microsoft Azure, or Rackspace), or by hiding data centers in Siberia and Tibet. All I need is satellite communication to the Internet from my laptop to be a supervillain. Indeed, I’m unlikely to get the bandwidth I need to process things on the plane. Instead, I’ll need to process everything on the Earth anyway, and send the low-bandwidth results to the plane.

In any case, if I were writing fiction, I’d have nuclear-powered airplanes that stayed aloft for months, operating out of remote bases in the Himalayas or Antartica.

EMP pulses

Small EMP pulse weapons exist, that’s not wholly fictional.

However, an EMP with the features, power, and effects in the movie is, of course, fictional. EMPs, even non-nuclear ones, are abused in films/TV so much that the Wikipedia pages on them spend a lot of time debunking them.

It would be cool if, one day, they used EMP realistically. In this movie, real missile-tipped with non-nuclear explosively-pumped flux compression generators could’ve been used for the same effect. Of course, simple explosives that blow up electronics also work.

Since hacking is the goto deus ex machina these days, they could’ve just had the hackers disable the power instead of using the EMP to do it.

Conclusion

In the movie, the hero uses his extraordinary driving skills to blow up a submarine. Given this level of willing disbelief, the exaggerated hacking is actually the least implausible bits of the movie. Indeed, as technology changes, making some of this more possible, the movie might be seen as predicting the future.

Security and the Internet of Things

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/02/security_and_th.html

Last year, on October 21, your digital video recorder ­- or at least a DVR like yours ­- knocked Twitter off the internet. Someone used your DVR, along with millions of insecure webcams, routers, and other connected devices, to launch an attack that started a chain reaction, resulting in Twitter, Reddit, Netflix, and many sites going off the internet. You probably didn’t realize that your DVR had that kind of power. But it does.

All computers are hackable. This has as much to do with the computer market as it does with the technologies. We prefer our software full of features and inexpensive, at the expense of security and reliability. That your computer can affect the security of Twitter is a market failure. The industry is filled with market failures that, until now, have been largely ignorable. As computers continue to permeate our homes, cars, businesses, these market failures will no longer be tolerable. Our only solution will be regulation, and that regulation will be foisted on us by a government desperate to “do something” in the face of disaster.

In this article I want to outline the problems, both technical and political, and point to some regulatory solutions. Regulation might be a dirty word in today’s political climate, but security is the exception to our small-government bias. And as the threats posed by computers become greater and more catastrophic, regulation will be inevitable. So now’s the time to start thinking about it.

We also need to reverse the trend to connect everything to the internet. And if we risk harm and even death, we need to think twice about what we connect and what we deliberately leave uncomputerized.

If we get this wrong, the computer industry will look like the pharmaceutical industry, or the aircraft industry. But if we get this right, we can maintain the innovative environment of the internet that has given us so much.

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We no longer have things with computers embedded in them. We have computers with things attached to them.

Your modern refrigerator is a computer that keeps things cold. Your oven, similarly, is a computer that makes things hot. An ATM is a computer with money inside. Your car is no longer a mechanical device with some computers inside; it’s a computer with four wheels and an engine. Actually, it’s a distributed system of over 100 computers with four wheels and an engine. And, of course, your phones became full-power general-purpose computers in 2007, when the iPhone was introduced.

We wear computers: fitness trackers and computer-enabled medical devices ­- and, of course, we carry our smartphones everywhere. Our homes have smart thermostats, smart appliances, smart door locks, even smart light bulbs. At work, many of those same smart devices are networked together with CCTV cameras, sensors that detect customer movements, and everything else. Cities are starting to embed smart sensors in roads, streetlights, and sidewalk squares, also smart energy grids and smart transportation networks. A nuclear power plant is really just a computer that produces electricity, and ­- like everything else we’ve just listed -­ it’s on the internet.

The internet is no longer a web that we connect to. Instead, it’s a computerized, networked, and interconnected world that we live in. This is the future, and what we’re calling the Internet of Things.

Broadly speaking, the Internet of Things has three parts. There are the sensors that collect data about us and our environment: smart thermostats, street and highway sensors, and those ubiquitous smartphones with their motion sensors and GPS location receivers. Then there are the “smarts” that figure out what the data means and what to do about it. This includes all the computer processors on these devices and ­- increasingly ­- in the cloud, as well as the memory that stores all of this information. And finally, there are the actuators that affect our environment. The point of a smart thermostat isn’t to record the temperature; it’s to control the furnace and the air conditioner. Driverless cars collect data about the road and the environment to steer themselves safely to their destinations.

You can think of the sensors as the eyes and ears of the internet. You can think of the actuators as the hands and feet of the internet. And you can think of the stuff in the middle as the brain. We are building an internet that senses, thinks, and acts.

This is the classic definition of a robot. We’re building a world-size robot, and we don’t even realize it.

To be sure, it’s not a robot in the classical sense. We think of robots as discrete autonomous entities, with sensors, brain, and actuators all together in a metal shell. The world-size robot is distributed. It doesn’t have a singular body, and parts of it are controlled in different ways by different people. It doesn’t have a central brain, and it has nothing even remotely resembling a consciousness. It doesn’t have a single goal or focus. It’s not even something we deliberately designed. It’s something we have inadvertently built out of the everyday objects we live with and take for granted. It is the extension of our computers and networks into the real world.

This world-size robot is actually more than the Internet of Things. It’s a combination of several decades-old computing trends: mobile computing, cloud computing, always-on computing, huge databases of personal information, the Internet of Things ­- or, more precisely, cyber-physical systems ­- autonomy, and artificial intelligence. And while it’s still not very smart, it’ll get smarter. It’ll get more powerful and more capable through all the interconnections we’re building.

It’ll also get much more dangerous.

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Computer security has been around for almost as long as computers have been. And while it’s true that security wasn’t part of the design of the original internet, it’s something we have been trying to achieve since its beginning.

I have been working in computer security for over 30 years: first in cryptography, then more generally in computer and network security, and now in general security technology. I have watched computers become ubiquitous, and have seen firsthand the problems ­- and solutions ­- of securing these complex machines and systems. I’m telling you all this because what used to be a specialized area of expertise now affects everything. Computer security is now everything security. There’s one critical difference, though: The threats have become greater.

Traditionally, computer security is divided into three categories: confidentiality, integrity, and availability. For the most part, our security concerns have largely centered around confidentiality. We’re concerned about our data and who has access to it ­- the world of privacy and surveillance, of data theft and misuse.

But threats come in many forms. Availability threats: computer viruses that delete our data, or ransomware that encrypts our data and demands payment for the unlock key. Integrity threats: hackers who can manipulate data entries can do things ranging from changing grades in a class to changing the amount of money in bank accounts. Some of these threats are pretty bad. Hospitals have paid tens of thousands of dollars to criminals whose ransomware encrypted critical medical files. JPMorgan Chase spends half a billion on cybersecurity a year.

Today, the integrity and availability threats are much worse than the confidentiality threats. Once computers start affecting the world in a direct and physical manner, there are real risks to life and property. There is a fundamental difference between crashing your computer and losing your spreadsheet data, and crashing your pacemaker and losing your life. This isn’t hyperbole; recently researchers found serious security vulnerabilities in St. Jude Medical’s implantable heart devices. Give the internet hands and feet, and it will have the ability to punch and kick.

Take a concrete example: modern cars, those computers on wheels. The steering wheel no longer turns the axles, nor does the accelerator pedal change the speed. Every move you make in a car is processed by a computer, which does the actual controlling. A central computer controls the dashboard. There’s another in the radio. The engine has 20 or so computers. These are all networked, and increasingly autonomous.

Now, let’s start listing the security threats. We don’t want car navigation systems to be used for mass surveillance, or the microphone for mass eavesdropping. We might want it to be used to determine a car’s location in the event of a 911 call, and possibly to collect information about highway congestion. We don’t want people to hack their own cars to bypass emissions-control limitations. We don’t want manufacturers or dealers to be able to do that, either, as Volkswagen did for years. We can imagine wanting to give police the ability to remotely and safely disable a moving car; that would make high-speed chases a thing of the past. But we definitely don’t want hackers to be able to do that. We definitely don’t want them disabling the brakes in every car without warning, at speed. As we make the transition from driver-controlled cars to cars with various driver-assist capabilities to fully driverless cars, we don’t want any of those critical components subverted. We don’t want someone to be able to accidentally crash your car, let alone do it on purpose. And equally, we don’t want them to be able to manipulate the navigation software to change your route, or the door-lock controls to prevent you from opening the door. I could go on.

That’s a lot of different security requirements, and the effects of getting them wrong range from illegal surveillance to extortion by ransomware to mass death.

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Our computers and smartphones are as secure as they are because companies like Microsoft, Apple, and Google spend a lot of time testing their code before it’s released, and quickly patch vulnerabilities when they’re discovered. Those companies can support large, dedicated teams because those companies make a huge amount of money, either directly or indirectly, from their software ­ and, in part, compete on its security. Unfortunately, this isn’t true of embedded systems like digital video recorders or home routers. Those systems are sold at a much lower margin, and are often built by offshore third parties. The companies involved simply don’t have the expertise to make them secure.

At a recent hacker conference, a security researcher analyzed 30 home routers and was able to break into half of them, including some of the most popular and common brands. The denial-of-service attacks that forced popular websites like Reddit and Twitter off the internet last October were enabled by vulnerabilities in devices like webcams and digital video recorders. In August, two security researchers demonstrated a ransomware attack on a smart thermostat.

Even worse, most of these devices don’t have any way to be patched. Companies like Microsoft and Apple continuously deliver security patches to your computers. Some home routers are technically patchable, but in a complicated way that only an expert would attempt. And the only way for you to update the firmware in your hackable DVR is to throw it away and buy a new one.

The market can’t fix this because neither the buyer nor the seller cares. The owners of the webcams and DVRs used in the denial-of-service attacks don’t care. Their devices were cheap to buy, they still work, and they don’t know any of the victims of the attacks. The sellers of those devices don’t care: They’re now selling newer and better models, and the original buyers only cared about price and features. There is no market solution, because the insecurity is what economists call an externality: It’s an effect of the purchasing decision that affects other people. Think of it kind of like invisible pollution.

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Security is an arms race between attacker and defender. Technology perturbs that arms race by changing the balance between attacker and defender. Understanding how this arms race has unfolded on the internet is essential to understanding why the world-size robot we’re building is so insecure, and how we might secure it. To that end, I have five truisms, born from what we’ve already learned about computer and internet security. They will soon affect the security arms race everywhere.

Truism No. 1: On the internet, attack is easier than defense.

There are many reasons for this, but the most important is the complexity of these systems. More complexity means more people involved, more parts, more interactions, more mistakes in the design and development process, more of everything where hidden insecurities can be found. Computer-security experts like to speak about the attack surface of a system: all the possible points an attacker might target and that must be secured. A complex system means a large attack surface. The defender has to secure the entire attack surface. The attacker just has to find one vulnerability ­- one unsecured avenue for attack -­ and gets to choose how and when to attack. It’s simply not a fair battle.

There are other, more general, reasons why attack is easier than defense. Attackers have a natural agility that defenders often lack. They don’t have to worry about laws, and often not about morals or ethics. They don’t have a bureaucracy to contend with, and can more quickly make use of technical innovations. Attackers also have a first-mover advantage. As a society, we’re generally terrible at proactive security; we rarely take preventive security measures until an attack actually happens. So more advantages go to the attacker.

Truism No. 2: Most software is poorly written and insecure.

If complexity isn’t enough, we compound the problem by producing lousy software. Well-written software, like the kind found in airplane avionics, is both expensive and time-consuming to produce. We don’t want that. For the most part, poorly written software has been good enough. We’d all rather live with buggy software than pay the prices good software would require. We don’t mind if our games crash regularly, or our business applications act weird once in a while. Because software has been largely benign, it hasn’t mattered. This has permeated the industry at all levels. At universities, we don’t teach how to code well. Companies don’t reward quality code in the same way they reward fast and cheap. And we consumers don’t demand it.

But poorly written software is riddled with bugs, sometimes as many as one per 1,000 lines of code. Some of them are inherent in the complexity of the software, but most are programming mistakes. Not all bugs are vulnerabilities, but some are.

Truism No. 3: Connecting everything to each other via the internet will expose new vulnerabilities.

The more we network things together, the more vulnerabilities on one thing will affect other things. On October 21, vulnerabilities in a wide variety of embedded devices were all harnessed together to create what hackers call a botnet. This botnet was used to launch a distributed denial-of-service attack against a company called Dyn. Dyn provided a critical internet function for many major internet sites. So when Dyn went down, so did all those popular websites.

These chains of vulnerabilities are everywhere. In 2012, journalist Mat Honan suffered a massive personal hack because of one of them. A vulnerability in his Amazon account allowed hackers to get into his Apple account, which allowed them to get into his Gmail account. And in 2013, the Target Corporation was hacked by someone stealing credentials from its HVAC contractor.

Vulnerabilities like these are particularly hard to fix, because no one system might actually be at fault. It might be the insecure interaction of two individually secure systems.

Truism No. 4: Everybody has to stop the best attackers in the world.

One of the most powerful properties of the internet is that it allows things to scale. This is true for our ability to access data or control systems or do any of the cool things we use the internet for, but it’s also true for attacks. In general, fewer attackers can do more damage because of better technology. It’s not just that these modern attackers are more efficient, it’s that the internet allows attacks to scale to a degree impossible without computers and networks.

This is fundamentally different from what we’re used to. When securing my home against burglars, I am only worried about the burglars who live close enough to my home to consider robbing me. The internet is different. When I think about the security of my network, I have to be concerned about the best attacker possible, because he’s the one who’s going to create the attack tool that everyone else will use. The attacker that discovered the vulnerability used to attack Dyn released the code to the world, and within a week there were a dozen attack tools using it.

Truism No. 5: Laws inhibit security research.

The Digital Millennium Copyright Act is a terrible law that fails at its purpose of preventing widespread piracy of movies and music. To make matters worse, it contains a provision that has critical side effects. According to the law, it is a crime to bypass security mechanisms that protect copyrighted work, even if that bypassing would otherwise be legal. Since all software can be copyrighted, it is arguably illegal to do security research on these devices and to publish the result.

Although the exact contours of the law are arguable, many companies are using this provision of the DMCA to threaten researchers who expose vulnerabilities in their embedded systems. This instills fear in researchers, and has a chilling effect on research, which means two things: (1) Vendors of these devices are more likely to leave them insecure, because no one will notice and they won’t be penalized in the market, and (2) security engineers don’t learn how to do security better.
Unfortunately, companies generally like the DMCA. The provisions against reverse-engineering spare them the embarrassment of having their shoddy security exposed. It also allows them to build proprietary systems that lock out competition. (This is an important one. Right now, your toaster cannot force you to only buy a particular brand of bread. But because of this law and an embedded computer, your Keurig coffee maker can force you to buy a particular brand of coffee.)

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In general, there are two basic paradigms of security. We can either try to secure something well the first time, or we can make our security agile. The first paradigm comes from the world of dangerous things: from planes, medical devices, buildings. It’s the paradigm that gives us secure design and secure engineering, security testing and certifications, professional licensing, detailed preplanning and complex government approvals, and long times-to-market. It’s security for a world where getting it right is paramount because getting it wrong means people dying.

The second paradigm comes from the fast-moving and heretofore largely benign world of software. In this paradigm, we have rapid prototyping, on-the-fly updates, and continual improvement. In this paradigm, new vulnerabilities are discovered all the time and security disasters regularly happen. Here, we stress survivability, recoverability, mitigation, adaptability, and muddling through. This is security for a world where getting it wrong is okay, as long as you can respond fast enough.

These two worlds are colliding. They’re colliding in our cars -­ literally -­ in our medical devices, our building control systems, our traffic control systems, and our voting machines. And although these paradigms are wildly different and largely incompatible, we need to figure out how to make them work together.

So far, we haven’t done very well. We still largely rely on the first paradigm for the dangerous computers in cars, airplanes, and medical devices. As a result, there are medical systems that can’t have security patches installed because that would invalidate their government approval. In 2015, Chrysler recalled 1.4 million cars to fix a software vulnerability. In September 2016, Tesla remotely sent a security patch to all of its Model S cars overnight. Tesla sure sounds like it’s doing things right, but what vulnerabilities does this remote patch feature open up?

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Until now we’ve largely left computer security to the market. Because the computer and network products we buy and use are so lousy, an enormous after-market industry in computer security has emerged. Governments, companies, and people buy the security they think they need to secure themselves. We’ve muddled through well enough, but the market failures inherent in trying to secure this world-size robot will soon become too big to ignore.

Markets alone can’t solve our security problems. Markets are motivated by profit and short-term goals at the expense of society. They can’t solve collective-action problems. They won’t be able to deal with economic externalities, like the vulnerabilities in DVRs that resulted in Twitter going offline. And we need a counterbalancing force to corporate power.

This all points to policy. While the details of any computer-security system are technical, getting the technologies broadly deployed is a problem that spans law, economics, psychology, and sociology. And getting the policy right is just as important as getting the technology right because, for internet security to work, law and technology have to work together. This is probably the most important lesson of Edward Snowden’s NSA disclosures. We already knew that technology can subvert law. Snowden demonstrated that law can also subvert technology. Both fail unless each work. It’s not enough to just let technology do its thing.

Any policy changes to secure this world-size robot will mean significant government regulation. I know it’s a sullied concept in today’s world, but I don’t see any other possible solution. It’s going to be especially difficult on the internet, where its permissionless nature is one of the best things about it and the underpinning of its most world-changing innovations. But I don’t see how that can continue when the internet can affect the world in a direct and physical manner.

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I have a proposal: a new government regulatory agency. Before dismissing it out of hand, please hear me out.

We have a practical problem when it comes to internet regulation. There’s no government structure to tackle this at a systemic level. Instead, there’s a fundamental mismatch between the way government works and the way this technology works that makes dealing with this problem impossible at the moment.

Government operates in silos. In the U.S., the FAA regulates aircraft. The NHTSA regulates cars. The FDA regulates medical devices. The FCC regulates communications devices. The FTC protects consumers in the face of “unfair” or “deceptive” trade practices. Even worse, who regulates data can depend on how it is used. If data is used to influence a voter, it’s the Federal Election Commission’s jurisdiction. If that same data is used to influence a consumer, it’s the FTC’s. Use those same technologies in a school, and the Department of Education is now in charge. Robotics will have its own set of problems, and no one is sure how that is going to be regulated. Each agency has a different approach and different rules. They have no expertise in these new issues, and they are not quick to expand their authority for all sorts of reasons.

Compare that with the internet. The internet is a freewheeling system of integrated objects and networks. It grows horizontally, demolishing old technological barriers so that people and systems that never previously communicated now can. Already, apps on a smartphone can log health information, control your energy use, and communicate with your car. That’s a set of functions that crosses jurisdictions of at least four different government agencies, and it’s only going to get worse.

Our world-size robot needs to be viewed as a single entity with millions of components interacting with each other. Any solutions here need to be holistic. They need to work everywhere, for everything. Whether we’re talking about cars, drones, or phones, they’re all computers.

This has lots of precedent. Many new technologies have led to the formation of new government regulatory agencies. Trains did, cars did, airplanes did. Radio led to the formation of the Federal Radio Commission, which became the FCC. Nuclear power led to the formation of the Atomic Energy Commission, which eventually became the Department of Energy. The reasons were the same in every case. New technologies need new expertise because they bring with them new challenges. Governments need a single agency to house that new expertise, because its applications cut across several preexisting agencies. It’s less that the new agency needs to regulate -­ although that’s often a big part of it -­ and more that governments recognize the importance of the new technologies.

The internet has famously eschewed formal regulation, instead adopting a multi-stakeholder model of academics, businesses, governments, and other interested parties. My hope is that we can keep the best of this approach in any regulatory agency, looking more at the new U.S. Digital Service or the 18F office inside the General Services Administration. Both of those organizations are dedicated to providing digital government services, and both have collected significant expertise by bringing people in from outside of government, and both have learned how to work closely with existing agencies. Any internet regulatory agency will similarly need to engage in a high level of collaborate regulation -­ both a challenge and an opportunity.

I don’t think any of us can predict the totality of the regulations we need to ensure the safety of this world, but here’s a few. We need government to ensure companies follow good security practices: testing, patching, secure defaults -­ and we need to be able to hold companies liable when they fail to do these things. We need government to mandate strong personal data protections, and limitations on data collection and use. We need to ensure that responsible security research is legal and well-funded. We need to enforce transparency in design, some sort of code escrow in case a company goes out of business, and interoperability between devices of different manufacturers, to counterbalance the monopolistic effects of interconnected technologies. Individuals need the right to take their data with them. And internet-enabled devices should retain some minimal functionality if disconnected from the internet

I’m not the only one talking about this. I’ve seen proposals for a National Institutes of Health analog for cybersecurity. University of Washington law professor Ryan Calo has proposed a Federal Robotics Commission. I think it needs to be broader: maybe a Department of Technology Policy.

Of course there will be problems. There’s a lack of expertise in these issues inside government. There’s a lack of willingness in government to do the hard regulatory work. Industry is worried about any new bureaucracy: both that it will stifle innovation by regulating too much and that it will be captured by industry and regulate too little. A domestic regulatory agency will have to deal with the fundamentally international nature of the problem.

But government is the entity we use to solve problems like this. Governments have the scope, scale, and balance of interests to address the problems. It’s the institution we’ve built to adjudicate competing social interests and internalize market externalities. Left to their own devices, the market simply can’t. That we’re currently in the middle of an era of low government trust, where many of us can’t imagine government doing anything positive in an area like this, is to our detriment.

Here’s the thing: Governments will get involved, regardless. The risks are too great, and the stakes are too high. Government already regulates dangerous physical systems like cars and medical devices. And nothing motivates the U.S. government like fear. Remember 2001? A nominally small-government Republican president created the Office of Homeland Security 11 days after the terrorist attacks: a rushed and ill-thought-out decision that we’ve been trying to fix for over a decade. A fatal disaster will similarly spur our government into action, and it’s unlikely to be well-considered and thoughtful action. Our choice isn’t between government involvement and no government involvement. Our choice is between smarter government involvement and stupider government involvement. We have to start thinking about this now. Regulations are necessary, important, and complex; and they’re coming. We can’t afford to ignore these issues until it’s too late.

We also need to start disconnecting systems. If we cannot secure complex systems to the level required by their real-world capabilities, then we must not build a world where everything is computerized and interconnected.

There are other models. We can enable local communications only. We can set limits on collected and stored data. We can deliberately design systems that don’t interoperate with each other. We can deliberately fetter devices, reversing the current trend of turning everything into a general-purpose computer. And, most important, we can move toward less centralization and more distributed systems, which is how the internet was first envisioned.

This might be a heresy in today’s race to network everything, but large, centralized systems are not inevitable. The technical elites are pushing us in that direction, but they really don’t have any good supporting arguments other than the profits of their ever-growing multinational corporations.

But this will change. It will change not only because of security concerns, it will also change because of political concerns. We’re starting to chafe under the worldview of everything producing data about us and what we do, and that data being available to both governments and corporations. Surveillance capitalism won’t be the business model of the internet forever. We need to change the fabric of the internet so that evil governments don’t have the tools to create a horrific totalitarian state. And while good laws and regulations in Western democracies are a great second line of defense, they can’t be our only line of defense.

My guess is that we will soon reach a high-water mark of computerization and connectivity, and that afterward we will make conscious decisions about what and how we decide to interconnect. But we’re still in the honeymoon phase of connectivity. Governments and corporations are punch-drunk on our data, and the rush to connect everything is driven by an even greater desire for power and market share. One of the presentations released by Edward Snowden contained the NSA mantra: “Collect it all.” A similar mantra for the internet today might be: “Connect it all.”

The inevitable backlash will not be driven by the market. It will be deliberate policy decisions that put the safety and welfare of society above individual corporations and industries. It will be deliberate policy decisions that prioritize the security of our systems over the demands of the FBI to weaken them in order to make their law-enforcement jobs easier. It’ll be hard policy for many to swallow, but our safety will depend on it.

**********

The scenarios I’ve outlined, both the technological and economic trends that are causing them and the political changes we need to make to start to fix them, come from my years of working in internet-security technology and policy. All of this is informed by an understanding of both technology and policy. That turns out to be critical, and there aren’t enough people who understand both.

This brings me to my final plea: We need more public-interest technologists.

Over the past couple of decades, we’ve seen examples of getting internet-security policy badly wrong. I’m thinking of the FBI’s “going dark” debate about its insistence that computer devices be designed to facilitate government access, the “vulnerability equities process” about when the government should disclose and fix a vulnerability versus when it should use it to attack other systems, the debacle over paperless touch-screen voting machines, and the DMCA that I discussed above. If you watched any of these policy debates unfold, you saw policy-makers and technologists talking past each other.

Our world-size robot will exacerbate these problems. The historical divide between Washington and Silicon Valley -­ the mistrust of governments by tech companies and the mistrust of tech companies by governments ­- is dangerous.

We have to fix this. Getting IoT security right depends on the two sides working together and, even more important, having people who are experts in each working on both. We need technologists to get involved in policy, and we need policy-makers to get involved in technology. We need people who are experts in making both technology and technological policy. We need technologists on congressional staffs, inside federal agencies, working for NGOs, and as part of the press. We need to create a viable career path for public-interest technologists, much as there already is one for public-interest attorneys. We need courses, and degree programs in colleges, for people interested in careers in public-interest technology. We need fellowships in organizations that need these people. We need technology companies to offer sabbaticals for technologists wanting to go down this path. We need an entire ecosystem that supports people bridging the gap between technology and law. We need a viable career path that ensures that even though people in this field won’t make as much as they would in a high-tech start-up, they will have viable careers. The security of our computerized and networked future ­ meaning the security of ourselves, families, homes, businesses, and communities ­ depends on it.

This plea is bigger than security, actually. Pretty much all of the major policy debates of this century will have a major technological component. Whether it’s weapons of mass destruction, robots drastically affecting employment, climate change, food safety, or the increasing ubiquity of ever-shrinking drones, understanding the policy means understanding the technology. Our society desperately needs technologists working on the policy. The alternative is bad policy.

**********

The world-size robot is less designed than created. It’s coming without any forethought or architecting or planning; most of us are completely unaware of what we’re building. In fact, I am not convinced we can actually design any of this. When we try to design complex sociotechnical systems like this, we are regularly surprised by their emergent properties. The best we can do is observe and channel these properties as best we can.

Market thinking sometimes makes us lose sight of the human choices and autonomy at stake. Before we get controlled ­ or killed ­ by the world-size robot, we need to rebuild confidence in our collective governance institutions. Law and policy may not seem as cool as digital tech, but they’re also places of critical innovation. They’re where we collectively bring about the world we want to live in.

While I might sound like a Cassandra, I’m actually optimistic about our future. Our society has tackled bigger problems than this one. It takes work and it’s not easy, but we eventually find our way clear to make the hard choices necessary to solve our real problems.

The world-size robot we’re building can only be managed responsibly if we start making real choices about the interconnected world we live in. Yes, we need security systems as robust as the threat landscape. But we also need laws that effectively regulate these dangerous technologies. And, more generally, we need to make moral, ethical, and political decisions on how those systems should work. Until now, we’ve largely left the internet alone. We gave programmers a special right to code cyberspace as they saw fit. This was okay because cyberspace was separate and relatively unimportant: That is, it didn’t matter. Now that that’s changed, we can no longer give programmers and the companies they work for this power. Those moral, ethical, and political decisions need, somehow, to be made by everybody. We need to link people with the same zeal that we are currently linking machines. “Connect it all” must be countered with “connect us all.”

This essay previously appeared in New York Magazine.

CES 2017: Trends For the Tech Savvy To Watch

Post Syndicated from Peter Cohen original https://www.backblaze.com/blog/ces-2017-trends-tech-savvy-watch/

This year’s Consumer Electronics Show (CES) just wrapped up in Las Vegas. The usual parade of cool tech toys created a lot of headlines this year, but there were some genuine trends to keep an eye on too. If you’re like us, you’re probably one of the first people around to adopt promising new technologies when they emerge. As early adopters we can sometimes lose the forest through the trees when it comes to understanding what this means for everyone else, so we’re going to look at it through that prism.

Alexa everywhere

2017 promises to be a big year for voice-activated “smart home” devices. The final landscape for this is still to be determined – all the expected players have their foot in it right now. Amazon, Apple, Google, Microsoft, even some smaller players.

Amazon deserves props after a holiday season that saw its Echo and Echo Dot devices in high demand. The company’s published an API that is Alexa is picking up plenty of support from third party manufacturers. Alexa’s testing for far beyond Echo, it seems.

Electronics giant LG is building Alexa into a line of robots designed for domestic duties and a refrigerator that also sports interior fridge cams, for example. Ford is integrating Alexa support into its Sync 3 automotive interface. Televisions, lighting devices, and home security products are among the many devices to feature Alexa integration.

Alexa is the new hotness, but the real trend here is in voice-assisted connectivity around the home. Even if Alexa runs out of steam, this tech is here to stay. The Internet of Things and voice activated interfaces are converging quickly, though that day isn’t today. It’s tantalizingly close. It’s still a niche, though, where it will stay for as long as consumers have to piece different things together to get it to work. That means there’s still room for disruption.

There’s especially ripe opportunity in underserved verticals. Take the home health market, for example: Natural language interfaces have huge implications for elderly and disabled care and assistance. Finding and developing solutions for those sorts of vertical markets is an awesome opportunity for the right players.

Of course, with great power comes great responsibility. A family of a six-year-old recently got stuck with a $160 bill after she told Alexa to order her cookies and a dollhouse. The family ended up donating the accidental order to charity. For what it’s worth, that problem can be avoided by activating a confirmation code feature in the Alexa software.

The Electric Vehicle (EV) Market Heats Up

One of the trickiest things to unpack from CES is hype from substance. Nowhere was that more apparent last week than the unveiling of Faraday Future’s FF91, a new Electric Vehicle (EV) positioned to go toe-to-toe with Tesla’s EV fleet.

The FF91 EV can purportedly go 378 miles on a single charge and also possesses autonomous driving capabilities (although its vaunted self-parking abilities didn’t demo as well as planned). When or if it’ll make it into production is still a head-scratcher, however. Faraday Future says it’ll be out next year, assuming that the company is beyond the production and manufacturing woes that have plagued it up until now.

While new vehicles and vehicle concepts are still largely the domain of auto shows, some auto manufacturers used CES to float new concepts ahead of the Detroit Auto Show, which happens this week. Toyota, for example, showed off its Concept-i, a car with artificial intelligence and natural language processing (like Siri or Alexa) designed to learn from you and adapt.

As we mentioned, Alexa is integrated into Ford’s Sync 3 platform, too. Already you can buy new cars with CarPlay and Android Auto, which makes it a lot easier to just talk with your mobile device to stay connected, get directions and entertain yourself on the morning commute simply by talking to your car instead of touching buttons. That’s a smart user interface change, but it’s still a potentially dangerous distraction for the driver. For this technology to succeed, it’s imperative that natural language interface designers make the experience as frictionless as possible.

Chrysler is making a play for future millennial families. We’re not making this up – they used “millennial” to describe the target market for this several times. The Portal concept is an electric minivan of sorts that’s chock-full of buzzwords: Facial recognition, Wi-Fi, media sharing, ten charging ports, semi-autonomous driving abilities and more).

2017 marks a pivot for car makers in this respect. For years the conventional wisdom that millennials were a lost cause for auto makers – Uber and Zipcar was all they needed. It turns out that was totally wrong. Economic pressures and diverse lifestyles may have delayed millennials’ trek toward auto ownership, but they’re turning out now in big numbers to buy wheels. Millennial families will need transportation just like generations before them back to the station wagon, which is why Chrysler says this “fifth-generation” family car will go into production sometime after 2018.

Volkswagen showed off its new I.D. concept car, a Golf-looking EV that also has all the requisite buzzwords. Speaking of buzzwords, what really excited us was the I.D. Buzz. This new EV resurrects the styling of the Hippy-era Microbus, with mood lighting, autonomous driving capabilities and a retractable steering wheel.

Rumors have persisted for years that VW was on the cusp of introducing a refreshed Microbus, but those rumors have never come to pass. And unfortunately, VW has no concrete plans to actually produce this – it seems to be a marketing effort to draw on nostalgic Boomer appeal, more than anything..

Both Buzz and Chrysler’s Portal do give us some insight about where auto makers are going when it comes to future generations of minivans: Electric, autonomous, customizable and more social than ever. If we are headed towards a future where vehicles drive themselves, family transportation will look very different than it is today.

Laptops At Both Extremes

CES saw the rollout of several new PC laptop models and concepts that will be hitting store shelves over the next several months.

Gamers looking for more real estate – a lot more real estate – were interested in Razer’s latest concept, Project Valerie. The laptop sports not one but three 4K displays which fold out on hinges. That’s 12K pixels of horizontal image space, mated to an Nvidia GeForce GTX 1080 graphics processor. A unibody aluminum chassis keeps it relatively thin (1.5 inches) when closed, but the entire rig weighs more than 12 pounds. Razer doesn’t have any immediate production plans, which may explain why their prototype was stolen before the end of the show.

Unlike Razer, Acer has production plans – immediate plans – for its gargantuan 21-inch Predator 21X laptop, priced at $8,999 and headed to store shelves next month. It was announced last year, but Acer finally offered launch details last week. A 17-inch model is also coming soon.

Big gaming laptops make for pretty pictures and certainly have their place in the PC ecosystem, but they’re niche devices. After a ramp up on 2-in-1s and low-powered laptops, Intel’s Kaby Lake processors are finally ready for the premium and mid-range laptop market. Kaby Lake efficiency improvements are helping PC makers build thinner and lighter laptops with better battery life, 4K video processing, faster solid state storage and more.

HP, Asus, MSI, Dell (and its gaming arm Alienware) were among the many companies with sleek new Kaby Lake-equipped models.

Gaming in the cloud with Nvidia

Nvidia, makers of premium graphics processors, offers GeForce Now cloud gaming to users of its Shield, an Android-based gaming handheld. That service is expanding to Windows and Mac in March.

Gaming as a Service, if you will, isn’t a new idea. OnLive pioneered the concept more than a decade ago. Gaikai followed, then was acquired by Sony in 2012. Nvidia’s had limited success with GeForce Now, but it’s been a single-platform offering up until now.

Nvidia has robust data centers to handle the processing and traffic, so best of luck to them as they scale up to meet demand. Gaming is very sensitive to network disruption – no gamer appreciates lag – so it’ll be interesting to see how GeForce Now scales to accommodate the new devices.

Mesh Networking

Mesh networking delivers more consistent, stronger network reception and performance than a conventional Wi-Fi router. Some of us have set up routers and extenders to fix dead spots – mesh networking works differently through smart traffic and better radio management between multiple network bases.

Eero, Ubiquiti, and even Google (with Google Wifi) are already offering mesh networking products, and this market segment looks to expand big in 2017. Netgear, Linksys, Asus, TP-Link and others are among those with new mesh networking setups. Mesh networking gear is still hampered by a higher price than plain old routers. That means the value isn’t there for some of us who have networking gear that gets the job done, even with shortcomings like dead zones or slow zones. But prices are coming down fast as more companies get into the market. If you have an 802.11ac router you’re happy with, stick with it for now, and move to a mesh networking setup for your next Wi-Fi upgrade.

Getting Your Feet Into VR

Our award for wackiest CES product has to go to Cerevo Taclim. Tactile feedback shoes and wireless hand controllers that help you “feel” the surface you’re walking on. Crunching snow underfoot, splashing through water. At an expected $1,000-$1,500 a pop, these probably won’t be next year’s Hatchimals, but it’s fun to imagine what game devs can do with the technology. Strap these to your feet then break out your best Hadouken in Street Fighter VR!

CES isn’t the real world. Only a fraction of what’s shown off ever sees the light of day, but it’s always interesting to see the trend-focused consumer electronics market shift and change from year to year. At the end of the year we hope to look back and see how much of this stuff ended up resonating with the actual consumer the show is named for.

The post CES 2017: Trends For the Tech Savvy To Watch appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

[$] GStreamer and the state of Linux desktop security

Post Syndicated from jake original http://lwn.net/Articles/708196/rss

Recently Chris
Evans
, an IT security expert currently working for Tesla, published a
series of blog posts about security vulnerabilities in the GStreamer
multimedia framework. A combination of the Chrome browser and GNOME-based
desktops creates a particularly
scary vulnerability
. Evans also made a provocative statement: that
vulnerabilities of this severity currently wouldn’t happen in
Windows 10. Is the state of security on the Linux desktop really that
bad — and what can be done about it?

Subscribers can click below for the full story from this week’s edition.

Orchestrating GPU-Accelerated Workloads on Amazon ECS

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/compute/orchestrating-gpu-accelerated-workloads-on-amazon-ecs/

My colleagues Brandon Chavis, Chad Schmutzer, and Pierre Steckmeyer sent a nice guest post that describes how to run GPU workloads on Amazon ECS.

It’s interesting to note that many workloads on Amazon ECS fit into three primary categories that have obvious synergy with containers:

  • PaaS
  • Batch workloads
  • Long-running services

While these are the most common workloads, we also see ECS used for a wide variety of applications. One new and interesting class of workload is GPU-accelerated workloads or, more specifically, workloads that need to leverage large amounts of GPUs across many nodes.

In this post, we take a look at how ECS enables GPU workloads. For example, at Amazon.com, the Amazon Personalization team runs significant machine learning workloads that leverage many GPUs on ECS.

Amazon ECS overview

ECS is a highly scalable, high performance service for running containers on AWS. ECS provides customers with a state management engine that offloads the heavy lifting of running your own orchestration software, while providing a number of integrations with other services across AWS. For example, you can assign IAM roles to individual tasks, use Auto Scaling to scale your containers in response to load across your services, and use the new Application Load Balancer to distribute load across your application, while automatically checking new tasks into the load balancer when Auto Scaling actions occur.

Today, customers run a wide variety of applications on Amazon ECS, and you can read about some of these use cases here:

Edmunds
Remind
Coursera

ECS and GPU workloads

When you log into Amazon.com, it’s important that you see recommendations for a product you might actually be interested in. The Amazon Personalization team generates personalized product recommendations for Amazon customers. In order to do this, they need to digest very large data sets containing information about the hundreds of millions of products (and just as many customers) that Amazon.com has.

The only way to handle this work in a reasonable amount of time is to ensure it is distributed across a very large number of machines. Amazon Personalization uses machine-learning software that leverages GPUs to train neural networks, but it is challenging to orchestrate this work across a very large number of GPU cores.

To overcome this challenge, Amazon Personalization uses ECS to manage a cluster of Amazon EC2 GPU instances. The team uses P2 instances, which include NVIDIA Tesla K80 GPUs. The cluster of P2 instances functions as a single pool of resources—aggregating CPU, memory, and GPUs—onto which machine learning work can be scheduled.

In order to run this work on an ECS cluster, a Docker image configured with NVIDIA CUDA drivers, which allow the container to communicate with the GPU hardware, is built and stored in Amazon EC2 Container Registry (Amazon ECR).

An ECS task definition is used to point to the container image in ECR and specify configuration for the container at runtime, such as how much CPU and memory each container should use, the command to run inside the container, if a data volume should be mounted in the container, where the source data set lives in Amazon S3, and so on.

After ECS is asked to run a Task, the ECS scheduler finds a suitable place to run the containers by identifying an instance in the cluster with available resources. As shown in the following architecture diagram, ECS can place containers into the cluster of EC2 GPU instances (“GPU slaves” in the diagram):

Give GPUs on ECS a try

To make it easier to try using GPUs on ECS, we’ve built an AWS CloudFormation template to alleviate much of the heavy lifting. This demo architecture is built around DSSTNE, the open source, machine learning library that the Amazon Personalization team uses to actually generate recommendations. Go to the GitHub repository to see the CloudFormation template.

The template spins up an ECS cluster with a single EC2 GPU instance in an Auto Scaling group. You can adjust the desired group capacity to run a larger cluster, if you’d like.

The instance is configured with all of the necessary software that DSSTNE requires for interaction with the underlying GPU hardware, such as NVIDIA drivers. The template also installs some development tools and libraries, like GCC, HDF5, and Open MPI so that you can compile the DSSTNE library at boot time. It then builds a Docker container with the DSSTNE library packaged up and uploads it to ECR. It copies the URL of the resulting container image in ECR and builds an ECS task definition that points to the container.

After the CloudFormation template completes, view the Outputs tab to get an idea of where to look for your new resources.

Conclusion

In this post, we explained how you can use ECS on high GPU workloads, and shared the CloudFormation template that makes it easy to get started with ECS and DSSTNE.

Unfortunately, it would take far too much page space to explain the details of the machine learning specifics in this post, but you can read the Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE post on the AWS Big Data Blog to learn more about how DSSTNE interacts with Apache Spark, trains models, generates predictions, and other fun machine learning concepts.

If you have questions or suggestions, please comment below.

New P2 Instance Type for Amazon EC2 – Up to 16 GPUs

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-p2-instance-type-for-amazon-ec2-up-to-16-gpus/

I like to watch long-term technology and business trends and watch as they shape the products and services that I get to use and to write about. As I was preparing to write today’s post, three such trends came to mind:

  • Moore’s Law – Coined in 1965, Moore’s Law postulates that the number of transistors on a chip doubles every year.
  • Mass Market / Mass Production – Because all of the technologies that we produce, use, and enjoy every day consume vast numbers of chips, there’s a huge market for them.
  • Specialization  – Due to the previous trend, even niche markets can be large enough to be addressed by purpose-built products.

As the industry pushes forward in accord with these trends, a couple of interesting challenges have surfaced over the past decade or so. Again, here’s a quick list (yes, I do think in bullet points):

  • Speed of Light – Even as transistor density increases, the speed of light imposes scaling limits (as computer pioneer Grace Hopper liked to point out, electricity can travel slightly less than 1 foot in a nanosecond).
  • Semiconductor Physics – Fundamental limits in the switching time (on/off) of a transistor ultimately determine the minimum achievable cycle time for a CPU.
  • Memory Bottlenecks – The well-known von Neumann Bottleneck imposes limits on the value of additional CPU power.

The GPU (Graphics Processing Unit) was born of these trends, and addresses many of the challenges! Processors have reached the upper bound on clock rates, but Moore’s Law gives designers more and more transistors to work with. Those transistors can be used to add more cache and more memory to a traditional architecture, but the von Neumann Bottleneck limits the value of doing so. On the other hand, we now have large markets for specialized hardware (gaming comes to mind as one of the early drivers for GPU consumption). Putting all of this together, the GPU scales out (more processors and parallel banks of memory) instead of up (faster processors and bottlenecked memory). Net-net: the GPU is an effective way to use lots of transistors to provide massive amounts of compute power!

With all of this as background, I would like to tell you about the newest EC2 instance type, the P2. These instances were designed to chew through tough, large-scale machine learning, deep learning, computational fluid dynamics (CFD), seismic analysis, molecular modeling, genomics, and computational finance workloads.

New P2 Instance Type
This new instance type incorporates up to 8 NVIDIA Tesla K80 Accelerators, each running a pair of NVIDIA GK210 GPUs. Each GPU provides 12 GB of memory (accessible via 240 GB/second of memory bandwidth), and 2,496 parallel processing cores. They also include ECC memory protection, allowing them to fix single-bit errors and to detect double-bit errors. The combination of ECC memory protection and double precision floating point operations makes these instances a great fit for all of the workloads that I mentioned above.

Here are the instance specs:

Instance Name GPU Count vCPU Count Memory Parallel Processing Cores
GPU Memory
Network Performance
p2.large 1 4 61 GiB 2,496 12 GB High
p2.8xlarge 8 32 488 GiB 19,968 96 GB 10 Gigabit
p2.16xlarge 16 64 732 GiB 39,936 192 GB 20 Gigabit

All of the instances are powered by an AWS-Specific version of Intel’s Broadwell processor, running at 2.7 GHz. The p2.16xlarge gives you control over C-states and P-states, and can turbo boost up to 3.0 GHz when running on 1 or 2 cores.

The GPUs support CUDA 7.5 and above, OpenCL 1.2, and the GPU Compute APIs. The GPUs on the p2.8xlarge and the p2.16xlarge are connected via a common PCI fabric. This allows for low-latency, peer to peer GPU to GPU transfers.

All of the instances make use of our new Enhanced Network Adapter (ENA – read Elastic Network Adapter – High Performance Network Interface for Amazon EC2 to learn more) and can, per the table above, support up to 20 Gbps of low-latency networking when used within a Placement Group.

Having a powerful multi-vCPU processor and multiple, well-connected GPUs on a single instance, along with low-latency access to other instances with the same features creates a very impressive hierarchy for scale-out processing:

  • One vCPU
  • Multiple vCPUs
  • One GPU
  • Multiple GPUs in an instance
  • Multiple GPUs in multiple instances within a Placement Group

P2 instances are VPC only, require the use of 64-bit, HVM-style, EBS-backed AMIs, and you can launch them today in the US East (Northern Virginia), US West (Oregon), and Europe (Ireland) regions as On-Demand Instances, Spot Instances, Reserved Instances, or Dedicated Hosts.

Here’s how I installed the NVIDIA drivers and the CUDA toolkit on my P2 instance, after first creating, formatting, attaching, and mounting (to /ebs) an EBS volume that had enough room for the CUDA toolkit and the associated samples (10 GiB is more than enough):

$ cd /ebs
$ sudo yum update -y
$ sudo yum groupinstall -y "Development tools"
$ sudo yum install -y kernel-devel-`uname -r`
$ wget http://us.download.nvidia.com/XFree86/Linux-x86_64/352.99/NVIDIA-Linux-x86_64-352.99.run
$ wget http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda_7.5.18_linux.run
$ chmod +x NVIDIA-Linux-x86_64-352.99.run
$ sudo ./NVIDIA-Linux-x86_64-352.99.run
$ chmod +x cuda_7.5.18_linux.run
$ sudo ./cuda_7.5.18_linux.run   # Don't install driver, just install CUDA and sample
$ sudo nvidia-smi -pm 1
$ sudo nvidia-smi -acp 0
$ sudo nvidia-smi --auto-boost-permission=0
$ sudo nvidia-smi -ac 2505,875

Note that NVIDIA-Linux-x86_64-352.99.run and cuda_7.5.18_linux.run are interactive programs; you need to accept the license agreements, choose some options, and enter some paths. Here’s how I set up the CUDA toolkit and the samples when I ran cuda_7.5.18_linux.run:

P2 and OpenCL in Action
With everything set up, I took this Gist and compiled it on a p2.8xlarge instance:

[[email protected] ~]$ gcc test.c -I /usr/local/cuda/include/ -L /usr/local/cuda-7.5/lib64/ -lOpenCL -o test

Here’s what it reported:

[[email protected] ~]$ ./test
1. Device: Tesla K80
 1.1 Hardware version: OpenCL 1.2 CUDA
 1.2 Software version: 352.99
 1.3 OpenCL C version: OpenCL C 1.2
 1.4 Parallel compute units: 13
2. Device: Tesla K80
 2.1 Hardware version: OpenCL 1.2 CUDA
 2.2 Software version: 352.99
 2.3 OpenCL C version: OpenCL C 1.2
 2.4 Parallel compute units: 13
3. Device: Tesla K80
 3.1 Hardware version: OpenCL 1.2 CUDA
 3.2 Software version: 352.99
 3.3 OpenCL C version: OpenCL C 1.2
 3.4 Parallel compute units: 13
4. Device: Tesla K80
 4.1 Hardware version: OpenCL 1.2 CUDA
 4.2 Software version: 352.99
 4.3 OpenCL C version: OpenCL C 1.2
 4.4 Parallel compute units: 13
5. Device: Tesla K80
 5.1 Hardware version: OpenCL 1.2 CUDA
 5.2 Software version: 352.99
 5.3 OpenCL C version: OpenCL C 1.2
 5.4 Parallel compute units: 13
6. Device: Tesla K80
 6.1 Hardware version: OpenCL 1.2 CUDA
 6.2 Software version: 352.99
 6.3 OpenCL C version: OpenCL C 1.2
 6.4 Parallel compute units: 13
7. Device: Tesla K80
 7.1 Hardware version: OpenCL 1.2 CUDA
 7.2 Software version: 352.99
 7.3 OpenCL C version: OpenCL C 1.2
 7.4 Parallel compute units: 13
8. Device: Tesla K80
 8.1 Hardware version: OpenCL 1.2 CUDA
 8.2 Software version: 352.99
 8.3 OpenCL C version: OpenCL C 1.2
 8.4 Parallel compute units: 13

As you can see, I have a ridiculous amount of compute power available at my fingertips!

New Deep Learning AMI
As I said at the beginning, these instances are a great fit for machine learning, deep learning, computational fluid dynamics (CFD), seismic analysis, molecular modeling, genomics, and computational finance workloads.

In order to help you to make great use of one or more P2 instances, we are launching a  Deep Learning AMI today. Deep learning has the potential to generate predictions (also known as scores or inferences) that are more reliable than those produced by less sophisticated machine learning, at the cost of a most complex and more computationally intensive training process. Fortunately, the newest generations of deep learning tools are able to distribute the training work across multiple GPUs on a single instance as well as across multiple instances each containing multiple GPUs.

The new AMI contains the following frameworks, each installed, configured, and tested against the popular MNIST database:

MXNet – This is a flexible, portable, and efficient library for deep learning. It supports declarative and imperative programming models across a wide variety of programming languages including C++, Python, R, Scala, Julia, Matlab, and JavaScript.

Caffe – This deep learning framework was designed with  expression, speed, and modularity in mind. It was developed at the Berkeley Vision and Learning Center (BVLC) with assistance from many community contributors.

Theano – This Python library allows you define, optimize, and evaluate mathematical expressions that involve multi-dimensional arrays.

TensorFlow – This is an open source library for numerical calculation using data flow graphs (each node in the graph represents a mathematical operation; each edge represents multidimensional data communicated between them).

Torch – This is a GPU-oriented scientific computing framework with support for machine learning algorithms, all accessible via LuaJIT.

Consult the README file in ~ec2-user/src to learn more about these frameworks.

AMIs from NVIDIA
You may also find the following AMIs to be of interest:


Jeff;

Tesla Hack – Remote Access Whilst Parked or Driving

Post Syndicated from Darknet original http://feedproxy.google.com/~r/darknethackers/~3/iSiN44TZlvw/

The big buzz on my Twitter this week was about the Tesla Hack carried out by a Chinese crew called Keen Security Lab. It’s no big surprise even though Tesla is known for being fairly security concious and proactive about it. With it being a connected car, that’s pretty important that any remote control capabilities […]

The post Tesla Hack…

Read the full post at darknet.org.uk