Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/07/traffic_analysi_1.html
Interesting research on home security cameras with cloud storage. Basically, attackers can learn very basic information about what’s going on in front of the camera, and infer when there is someone home.
News article.
Slashdot thread.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/01/modern_mass_sur.html
Communities across the United States are starting to ban facial recognition technologies. In May of last year, San Francisco banned facial recognition; the neighboring city of Oakland soon followed, as did Somerville and Brookline in Massachusetts (a statewide ban may follow). In December, San Diego suspended a facial recognition program in advance of a new statewide law, which declared it illegal, coming into effect. Forty major music festivals pledged not to use the technology, and activists are calling for a nationwide ban. Many Democratic presidential candidates support at least a partial ban on the technology.
These efforts are well-intentioned, but facial recognition bans are the wrong way to fight against modern surveillance. Focusing on one particular identification method misconstrues the nature of the surveillance society we’re in the process of building. Ubiquitous mass surveillance is increasingly the norm. In countries like China, a surveillance infrastructure is being built by the government for social control. In countries like the United States, it’s being built by corporations in order to influence our buying behavior, and is incidentally used by the government.
In all cases, modern mass surveillance has three broad components: identification, correlation and discrimination. Let’s take them in turn.
Facial recognition is a technology that can be used to identify people without their knowledge or consent. It relies on the prevalence of cameras, which are becoming both more powerful and smaller, and machine learning technologies that can match the output of these cameras with images from a database of existing photos.
But that’s just one identification technology among many. People can be identified at a distance by their heartbeat or by their gait, using a laser-based system. Cameras are so good that they can read fingerprints and iris patterns from meters away. And even without any of these technologies, we can always be identified because our smartphones broadcast unique numbers called MAC addresses. Other things identify us as well: our phone numbers, our credit card numbers, the license plates on our cars. China, for example, uses multiple identification technologies to support its surveillance state.
Once we are identified, the data about who we are and what we are doing can be correlated with other data collected at other times. This might be movement data, which can be used to “follow” us as we move throughout our day. It can be purchasing data, Internet browsing data, or data about who we talk to via email or text. It might be data about our income, ethnicity, lifestyle, profession and interests. There is an entire industry of data brokers who make a living analyzing and augmenting data about who we are – using surveillance data collected by all sorts of companies and then sold without our knowledge or consent.
There is a huge – and almost entirely unregulated – data broker industry in the United States that trades on our information. This is how large Internet companies like Google and Facebook make their money. It’s not just that they know who we are, it’s that they correlate what they know about us to create profiles about who we are and what our interests are. This is why many companies buy license plate data from states. It’s also why companies like Google are buying health records, and part of the reason Google bought the company Fitbit, along with all of its data.
The whole purpose of this process is for companies – and governments – to treat individuals differently. We are shown different ads on the Internet and receive different offers for credit cards. Smart billboards display different advertisements based on who we are. In the future, we might be treated differently when we walk into a store, just as we currently are when we visit websites.
The point is that it doesn’t matter which technology is used to identify people. That there currently is no comprehensive database of heartbeats or gaits doesn’t make the technologies that gather them any less effective. And most of the time, it doesn’t matter if identification isn’t tied to a real name. What’s important is that we can be consistently identified over time. We might be completely anonymous in a system that uses unique cookies to track us as we browse the Internet, but the same process of correlation and discrimination still occurs. It’s the same with faces; we can be tracked as we move around a store or shopping mall, even if that tracking isn’t tied to a specific name. And that anonymity is fragile: If we ever order something online with a credit card, or purchase something with a credit card in a store, then suddenly our real names are attached to what was anonymous tracking information.
Regulating this system means addressing all three steps of the process. A ban on facial recognition won’t make any difference if, in response, surveillance systems switch to identifying people by smartphone MAC addresses. The problem is that we are being identified without our knowledge or consent, and society needs rules about when that is permissible.
Similarly, we need rules about how our data can be combined with other data, and then bought and sold without our knowledge or consent. The data broker industry is almost entirely unregulated; there’s only one law – passed in Vermont in 2018 – that requires data brokers to register and explain in broad terms what kind of data they collect. The large Internet surveillance companies like Facebook and Google collect dossiers on us are more detailed than those of any police state of the previous century. Reasonable laws would prevent the worst of their abuses.
Finally, we need better rules about when and how it is permissible for companies to discriminate. Discrimination based on protected characteristics like race and gender is already illegal, but those rules are ineffectual against the current technologies of surveillance and control. When people can be identified and their data correlated at a speed and scale previously unseen, we need new rules.
Today, facial recognition technologies are receiving the brunt of the tech backlash, but focusing on them misses the point. We need to have a serious conversation about all the technologies of identification, correlation and discrimination, and decide how much we as a society want to be spied on by governments and corporations — and what sorts of influence we want them to have over our lives.
This essay previously appeared in the New York Times.
EDITED TO ADD: Rereading this post-publication, I see that it comes off as overly critical of those who are doing activism in this space. Writing the piece, I wasn’t thinking about political tactics. I was thinking about the technologies that support surveillance capitalism, and law enforcement’s usage of that corporate platform. Of course it makes sense to focus on face recognition in the short term. It’s something that’s easy to explain, viscerally creepy, and obviously actionable. It also makes sense to focus specifically on law enforcement’s use of the technology; there are clear civil and constitutional rights issues. The fact that law enforcement is so deeply involved in the technology’s marketing feels wrong. And the technology is currently being deployed in Hong Kong against political protesters. It’s why the issue has momentum, and why we’ve gotten the small wins we’ve had. (The EU is considering a five-year ban on face recognition technologies.) Those wins build momentum, which lead to more wins. I should have been kinder to those in the trenches.
If you want to help, sign the petition from Public Voice calling on a moratorium on facial recognition technology for mass surveillance. Or write to your US congressperson and demand similar action. There’s more information from EFF and EPIC.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/01/police_surveill.html
Special Services Group, a company that sells surveillance tools to the FBI, DEA, ICE, and other US government agencies, has had its secret sales brochure published. Motherboard received the brochure as part of a FOIA request to the Irvine Police Department in California.
“The Tombstone Cam is our newest video concealment offering the ability to conduct remote surveillance operations from cemeteries,” one section of the Black Book reads. The device can also capture audio, its battery can last for two days, and “the Tombstone Cam is fully portable and can be easily moved from location to location as necessary,” the brochure adds. Another product is a video and audio capturing device that looks like an alarm clock, suitable for “hotel room stings,” and other cameras are designed to appear like small tree trunks and rocks, the brochure reads.
The “Shop-Vac Covert DVR Recording System” is essentially a camera and 1TB harddrive hidden inside a vacuum cleaner. “An AC power connector is available for long-term deployments, and DC power options can be connected for mobile deployments also,” the brochure reads. The description doesn’t say whether the vacuum cleaner itself works.
[…]
One of the company’s “Rapid Vehicle Deployment Kits” includes a camera hidden inside a baby car seat. “The system is fully portable, so you are not restricted to the same drop car for each mission,” the description adds.
[…]
The so-called “K-MIC In-mouth Microphone & Speaker Set” is a tiny Bluetooth device that sits on a user’s teeth and allows them to “communicate hands-free in crowded, noisy surroundings” with “near-zero visual indications,” the Black Book adds.
Other products include more traditional surveillance cameras and lenses as well as tools for surreptitiously gaining entry to buildings. The “Phantom RFID Exploitation Toolkit” lets a user clone an access card or fob, and the so-called “Shadow” product can “covertly provide the user with PIN code to an alarm panel,” the brochure reads.
The Motherboard article also reprints the scary emails Motherboard received from Special Services Group, when asked for comment. Of course, Motherboard published the information anyway.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/12/cameras_that_au.html
New South Wales is implementing a camera system that automatically detects when a driver is using a mobile phone.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/07/zoom_vulnerabil.html
The Zoom conferencing app has a vulnerability that allows someone to remotely take over the computer’s camera.
It’s a bad vulnerability, made worse by the fact that it remains even if you uninstall the Zoom app:
This vulnerability allows any website to forcibly join a user to a Zoom call, with their video camera activated, without the user’s permission.
On top of this, this vulnerability would have allowed any webpage to DOS (Denial of Service) a Mac by repeatedly joining a user to an invalid call.
Additionally, if you’ve ever installed the Zoom client and then uninstalled it, you still have a localhost web server on your machine that will happily re-install the Zoom client for you, without requiring any user interaction on your behalf besides visiting a webpage. This re-install ‘feature’ continues to work to this day.
Zoom didn’t take the vulnerability seriously:
This vulnerability was originally responsibly disclosed on March 26, 2019. This initial report included a proposed description of a ‘quick fix’ Zoom could have implemented by simply changing their server logic. It took Zoom 10 days to confirm the vulnerability. The first actual meeting about how the vulnerability would be patched occurred on June 11th, 2019, only 18 days before the end of the 90-day public disclosure deadline. During this meeting, the details of the vulnerability were confirmed and Zoom’s planned solution was discussed. However, I was very easily able to spot and describe bypasses in their planned fix. At this point, Zoom was left with 18 days to resolve the vulnerability. On June 24th after 90 days of waiting, the last day before the public disclosure deadline, I discovered that Zoom had only implemented the ‘quick fix’ solution originally suggested.
This is why we disclose vulnerabilities. Now, finally, Zoom is taking this seriously and fixing it for real.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/computers_and_video.html
It used to be that surveillance cameras were passive. Maybe they just recorded, and no one looked at the video unless they needed to. Maybe a bored guard watched a dozen different screens, scanning for something interesting. In either case, the video was only stored for a few days because storage was expensive.
Increasingly, none of that is true. Recent developments in video analytics — fueled by artificial intelligence techniques like machine learning — enable computers to watch and understand surveillance videos with human-like discernment. Identification technologies make it easier to automatically figure out who is in the videos. And finally, the cameras themselves have become cheaper, more ubiquitous, and much better; cameras mounted on drones can effectively watch an entire city. Computers can watch all the video without human issues like distraction, fatigue, training, or needing to be paid. The result is a level of surveillance that was impossible just a few years ago.
An ACLU report published Thursday called “the Dawn of Robot Surveillance” says AI-aided video surveillance “won’t just record us, but will also make judgments about us based on their understanding of our actions, emotions, skin color, clothing, voice, and more. These automated ‘video analytics’ technologies threaten to fundamentally change the nature of surveillance.”
Let’s take the technologies one at a time. First: video analytics. Computers are getting better at recognizing what’s going on in a video. Detecting when a person or vehicle enters a forbidden area is easy. Modern systems can alarm when someone is walking in the wrong direction — going in through an exit-only corridor, for example. They can count people or cars. They can detect when luggage is left unattended, or when previously unattended luggage is picked up and removed. They can detect when someone is loitering in an area, is lying down, or is running. Increasingly, they can detect particular actions by people. Amazon’s cashier-less stores rely on video analytics to figure out when someone picks an item off a shelf and doesn’t put it back.
More than identifying actions, video analytics allow computers to understand what’s going on in a video: They can flag people based on their clothing or behavior, identify people’s emotions through body language and behavior, and find people who are acting “unusual” based on everyone else around them. Those same Amazon in-store cameras can analyze customer sentiment. Other systems can describe what’s happening in a video scene.
Computers can also identify people. AIs are getting better at identifying people in those videos. Facial recognition technology is improving all the time, made easier by the enormous stockpile of tagged photographs we give to Facebook and other social media sites, and the photos governments collect in the process of issuing ID cards and drivers licenses. The technology already exists to automatically identify everyone a camera “sees” in real time. Even without video identification, we can be identified by the unique information continuously broadcasted by the smartphones we carry with us everywhere, or by our laptops or Bluetooth-connected devices. Police have been tracking phones for years, and this practice can now be combined with video analytics.
Once a monitoring system identifies people, their data can be combined with other data, either collected or purchased: from cell phone records, GPS surveillance history, purchasing data, and so on. Social media companies like Facebook have spent years learning about our personalities and beliefs by what we post, comment on, and “like.” This is “data inference,” and when combined with video it offers a powerful window into people’s behaviors and motivations.
Camera resolution is also improving. Gigapixel cameras as so good that they can capture individual faces and identify license places in photos taken miles away. “Wide-area surveillance” cameras can be mounted on airplanes and drones, and can operate continuously. On the ground, cameras can be hidden in street lights and other regular objects. In space, satellite cameras have also dramatically improved.
Data storage has become incredibly cheap, and cloud storage makes it all so easy. Video data can easily be saved for years, allowing computers to conduct all of this surveillance backwards in time.
In democratic countries, such surveillance is marketed as crime prevention — or counterterrorism. In countries like China, it is blatantly used to suppress political activity and for social control. In all instances, it’s being implemented without a lot of public debate by law-enforcement agencies and by corporations in public spaces they control.
This is bad, because ubiquitous surveillance will drastically change our relationship to society. We’ve never lived in this sort of world, even those of us who have lived through previous totalitarian regimes. The effects will be felt in many different areas. False positives — when the surveillance system gets it wrong — will lead to harassment and worse. Discrimination will become automated. Those who fall outside norms will be marginalized. And most importantly, the inability to live anonymously will have an enormous chilling effect on speech and behavior, which in turn will hobble society’s ability to experiment and change. A recent ACLU report discusses these harms in more depth. While it’s possible that some of this surveillance is worth the trade-offs, we as society need to deliberately and intelligently make decisions about it.
Some jurisdictions are starting to notice. Last month, San Francisco became the first city to ban facial recognition technology by police and other government agencies. A similar ban is being considered in Somerville, MA, and Oakland, CA. These are exceptions, and limited to the more liberal areas of the country.
We often believe that technological change is inevitable, and that there’s nothing we can do to stop it — or even to steer it. That’s simply not true. We’re led to believe this because we don’t often see it, understand it, or have a say in how or when it is deployed. The problem is that technologies of cameras, resolution, machine learning, and artificial intelligence are complex and specialized.
Laws like what was just passed in San Francisco won’t stop the development of these technologies, but they’re not intended to. They’re intended as pauses, so our policy making can catch up with technology. As a general rule, the US government tends to ignore technologies as they’re being developed and deployed, so as not to stifle innovation. But as the rate of technological change increases, so does the unanticipated effects on our lives. Just as we’ve been surprised by the threats to democracy caused by surveillance capitalism, AI-enabled video surveillance will have similar surprising effects. Maybe a pause in our headlong deployment of these technologies will allow us the time to discuss what kind of society we want to live in, and then enact rules to bring that kind of society about.
This essay previously appeared on Vice Motherboard.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/video_surveilla.html
The ACLU’s Jay Stanley has just published a fantastic report: “The Dawn of Robot Surveillance” (blog post here) Basically, it lays out a future of ubiquitous video cameras watched by increasingly sophisticated video analytics software, and discusses the potential harms to society.
I’m not going to excerpt a piece, because you really need to read the whole thing.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/06/ios_shortcut_fo.html
“Hey Siri; I’m getting pulled over” can be a shortcut:
Once the shortcut is installed and configured, you just have to say, for example, “Hey Siri, I’m getting pulled over.” Then the program pauses music you may be playing, turns down the brightness on the iPhone, and turns on “do not disturb” mode.
It also sends a quick text to a predetermined contact to tell them you’ve been pulled over, and it starts recording using the iPhone’s front-facing camera. Once you’ve stopped recording, it can text or email the video to a different predetermined contact and save it to Dropbox.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/12/stealing_nativi.html
The New York Times is reporting on the security measures people are using to protect nativity displays.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/11/hidden_cameras_.html
Both the US Drug Enforcement Administration (DEA) and Immigration and Customs Enforcement (ICE) are hiding surveillance cameras in streetlights.
According to government procurement data, the DEA has paid a Houston, Texas company called Cowboy Streetlight Concealments LLC roughly $22,000 since June 2018 for “video recording and reproducing equipment.” ICE paid out about $28,000 to Cowboy Streetlight Concealments over the same period of time.
It’s unclear where the DEA and ICE streetlight cameras have been installed, or where the next deployments will take place. ICE offices in Dallas, Houston, and San Antonio have provided funding for recent acquisitions from Cowboy Streetlight Concealments; the DEA’s most recent purchases were funded by the agency’s Office of Investigative Technology, which is located in Lorton, Virginia.
Fifty thousand dollars doesn’t buy a lot of streetlight surveillance cameras, so either this is a pilot program or there are a lot more procurements elsewhere that we don’t know about.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/11/consumer_report_1.html
Consumer Reports is starting to evaluate the security of IoT devices. As part of that, it’s reviewing wireless home-security cameras.
It found significant security vulnerabilities in D-Link cameras:
In contrast, D-Link doesn’t store video from the DCS-2630L in the cloud. Instead, the camera has its own, onboard web server, which can deliver video to the user in different ways.
Users can view the video using an app, mydlink Lite. The video is encrypted, and it travels from the camera through D-Link’s corporate servers, and ultimately to the user’s phone. Users can also access the same encrypted video feed through a company web page, mydlink.com. Those are both secure methods of accessing the video.
But the D-Link camera also lets you bypass the D-Link corporate servers and access the video directly through a web browser on a laptop or other device. If you do this, the web server on the camera doesn’t encrypt the video.
If you set up this kind of remote access, the camera and unencrypted video is open to the web. They could be discovered by anyone who finds or guesses the camera’s IP address — and if you haven’t set a strong password, a hacker might find it easy to gain access.
The real news is that Consumer Reports is able to put pressure on device manufacturers:
In response to a Consumer Reports query, D-Link said that security would be tightened through updates this fall. Consumer Reports will evaluate those updates once they are available.
This is the sort of sustained pressure we need on IoT device manufacturers.
Boing Boing link.
EDITED TO ADD (11/13): In related news, the US Federal Trade Commission is suing D-Link because their routers are so insecure. The lawsuit was filed in January 2017.
Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/take-a-photo-of-yourself-unreliable-cartoon/
Take a selfie, wait for the image to appear, and behold a cartoon version of yourself. Or, at least, behold a cartoon version of whatever the camera thought it saw. Welcome to Draw This by maker Dan Macnish.
Dan has made code, instructions, and wiring diagrams available to help you bring this beguiling weirdery into your own life.
One of the fun things about this re-imagined polaroid is that you never get to see the original image. You point, and shoot – and out pops a cartoon; the camera’s best interpretation of what it saw. The result is always a surprise. A food selfie of a healthy salad might turn into an enormous hot dog, or a photo with friends might be photobombed by a goat.
OK. Let’s take this one step at a time.
Draw This uses a Raspberry Pi 3 and a Camera Module, with a button and a useful status LED connected to the GPIO pins via a breadboard. You press the button, and the camera captures a still image while the LED comes on and stays lit for a couple of seconds while the Pi processes the image. So far, so standard Pi camera build.
Dan uses Python to process the captured photograph, employing a pre-trained machine learning model from Google to recognise multiple objects in the image. Now he brings the strangeness. The Pi matches the things it sees in the photo with doodles from Google’s huge open-source Quick, Draw! dataset, and generates a new image that represents the objects in the original image as doodles. Then a thermal printer connected to the Pi’s GPIO pins prints the results.
Kangaroos from the Quick, Draw! dataset (I got distracted)
Reading about this build leaves me yearning to see its oddest interpretation of a scene, so if you make this and you find it really does turn you or your friend into a goat, please do share that with us.
And as you can see from my kangaroo digression above, there is a ton of potential for bizarro makes that use the Quick, Draw! dataset, object recognition models, or both; it’s not just the marsupials that are inexplicably compelling (I dare you to go and look and see how long it takes you to get back to whatever you were in the middle of). If you’re planning to make this, or something inspired by this, check out Dan’s cartoonify GitHub repo. And tell us all about it in the comments.
The post Take a photo of yourself as an unreliable cartoon appeared first on Raspberry Pi.
Post Syndicated from Philip Colligan original https://www.raspberrypi.org/blog/working-with-scout-association-digital-skills-for-life/
Today we’re launching a new partnership between the Scouts and the Raspberry Pi Foundation that will help tens of thousands of young people learn crucial digital skills for life. In this blog post, I want to explain what we’ve got planned, why it matters, and how you can get involved.
First, let me tell you why this partnership matters to me. As a child growing up in North Wales in the 1980s, Scouting changed my life. My time with 2nd Rhyl provided me with countless opportunities to grow and develop new skills. It taught me about teamwork and community in ways that continue to shape my decisions today.
As my own kids (now seven and ten) have joined Scouting, I’ve seen the same opportunities opening up for them, and like so many parents, I’ve come back to the movement as a volunteer to support their local section. So this is deeply personal for me, and the same is true for many of my colleagues at the Raspberry Pi Foundation who in different ways have been part of the Scouting movement.
That shouldn’t come as a surprise. Scouting and Raspberry Pi share many of the same values. We are both community-led movements that aim to help young people develop the skills they need for life. We are both powered by an amazing army of volunteers who give their time to support that mission. We both care about inclusiveness, and pride ourselves on combining fun with learning by doing.
Raspberry Pi started life in 2008 as a response to the problem that too many young people were growing up without the skills to create with technology. Our goal is that everyone should be able to harness the power of computing and digital technologies, for work, to solve problems that matter to them, and to express themselves creatively.
In 2012 we launched our first product, the world’s first $35 computer. Just six years on, we have sold over 20 million Raspberry Pi computers and helped kickstart a global movement for digital skills.
The Raspberry Pi Foundation now runs the world’s largest network of volunteer-led computing clubs (Code Clubs and CoderDojos), and creates free educational resources that are used by millions of young people all over the world to learn how to create with digital technologies. And lots of what we are able to achieve is because of partnerships with fantastic organisations that share our goals. For example, through our partnership with the European Space Agency, thousands of young people have written code that has run on two Raspberry Pi computers that Tim Peake took to the International Space Station as part of his Mission Principia.
Today we’re launching the new Digital Maker Staged Activity Badge to help tens of thousands of young people learn how to create with technology through Scouting. Over the past few months, we’ve been working with the Scouts all over the UK to develop and test the new badge requirements, along with guidance, project ideas, and resources that really make them work for Scouting. We know that we need to get two things right: relevance and accessibility.
Relevance is all about making sure that the activities and resources we provide are a really good fit for Scouting and Scouting’s mission to equip young people with skills for life. From the digital compass to nature cameras and the reinvented wide game, we’ve had a lot of fun thinking about ways we can bring to life the crucial role that digital technologies can play in the outdoors and adventure.
Compass Coding with Raspberry Pi
We are beyond excited to be launching a new partnership with the Raspberry Pi Foundation, which will help tens of thousands of young people learn digital skills for life.
We also know that there are great opportunities for Scouts to use digital technologies to solve social problems in their communities, reflecting the movement’s commitment to social action. Today we’re launching the first set of project ideas and resources, with many more to follow over the coming weeks and months.
Accessibility is about providing every Scout leader with the confidence, support, and kit to enable them to offer the Digital Maker Staged Activity Badge to their young people. A lot of work and care has gone into designing activities that require very little equipment: for example, activities at Stages 1 and 2 can be completed with a laptop without access to the internet. For the activities that do require kit, we will be working with Scout Stores and districts to make low-cost kit available to buy or loan.
We’re producing accessible instructions, worksheets, and videos to help leaders run sessions with confidence, and we’ll also be planning training for leaders. We will work with our network of Code Clubs and CoderDojos to connect them with local sections to organise joint activities, bringing both kit and expertise along with them.
Today’s launch is just the start. We’ll be developing our partnership over the next few years, and we can’t wait for you to join us in getting more young people making things with technology.
Take a look at the brand-new Raspberry Pi resources designed especially for Scouts, to get young people making and creating right away.
The post Working with the Scout Association on digital skills for life appeared first on Raspberry Pi.
Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/how-to-leak-securely-for-white-house.html
Spencer Ackerman has this interesting story about a guy assigned to crack down on unauthorized White House leaks. It’s necessarily light on technical details, so I thought I’d write up some guesses, either as a guide for future reporters asking questions, or for people who want to better know the risks when leak information.
Post Syndicated from Ramkishore Bhattacharyya original https://aws.amazon.com/blogs/security/how-to-eliminate-the-need-for-hardcoded-aws-credentials-in-devices-by-using-the-aws-iot-credentials-provider/
The Internet of Things (IoT) has precipitated to an influx of connected devices and data that can be mined to gain useful business insights. If you own an IoT device, you might want the data to be uploaded seamlessly from your connected devices to the cloud so that you can make use of cloud storage and the processing power to perform sophisticated analysis of data. To upload the data to the AWS Cloud, devices must pass authentication and authorization checks performed by the respective AWS services. The standard way of authenticating AWS requests is the Signature Version 4 algorithm that requires the caller to have an access key ID and secret access key. Consequently, you need to hardcode the access key ID and the secret access key on your devices. Alternatively, you can use the built-in X.509 certificate as the unique device identity to authenticate AWS requests.
AWS IoT has introduced the credentials provider feature that allows a caller to authenticate AWS requests by having an X.509 certificate. The credentials provider authenticates a caller using an X.509 certificate, and vends a temporary, limited-privilege security token. The token can be used to sign and authenticate any AWS request. Thus, the credentials provider relieves you from having to manage and periodically refresh the access key ID and secret access key remotely on your devices.
In the process of retrieving a security token, you use AWS IoT to create a thing (a representation of a specific device or logical entity), register a certificate, and create AWS IoT policies. You also configure an AWS Identity and Access Management (IAM) role and attach appropriate IAM policies to the role so that the credentials provider can assume the role on your behalf. You also make an HTTP-over-Transport Layer Security (TLS) mutual authentication request to the credentials provider that uses your preconfigured thing, certificate, policies, and IAM role to authenticate and authorize the request, and obtain a security token on your behalf. You can then use the token to sign any AWS request using Signature Version 4.
In this blog post, I explain the AWS IoT credentials provider design and then demonstrate the end-to-end process of retrieving a security token from AWS IoT and using the token to write a temperature and humidity record to a specific Amazon DynamoDB table.
Note: This post assumes you are familiar with AWS IoT and IAM to perform steps using the AWS CLI and OpenSSL. Make sure you are running the latest version of the AWS CLI.
The following numbered diagram illustrates the credentials provider workflow. The diagram is followed by explanations of the steps.
To explain the steps of the workflow as illustrated in the preceding diagram:
In another solution, you could configure an AWS Lambda rule that ingests your device data and sends it to another AWS service. However, in applications that require the uploading of large files such as videos or aggregated telemetry to the AWS Cloud, you may want your devices to be able to authenticate and send data directly to the AWS service of your choice. The credentials provider enables you to do that.
Perform the following steps as part of this solution:
Register your home thermostat in the AWS IoT thing registry database by creating a thing type and a thing. You can use the AWS CLI with the following command to create a thing type. The thing type allows you to store description and configuration information that is common to a set of things.
The following is sample output of the create-thing-type command. It contains the thingTypeName, thingTypeId, and thingTypeArn.
Run the following command in the AWS CLI to create a thing.
The following is sample output of the create-thing command. It contains the thingArn, thingName, and thingId.
Now, you need to have a Certificate Authority (CA) certificate, sign a device certificate using the CA certificate, and register both certificates with AWS IoT before your device can authenticate to AWS IoT. If you do not already have a CA certificate, you can use OpenSSL to create a CA certificate, as described in Use Your Own Certificate. To register your CA certificate with AWS IoT, follow the steps on Registering Your CA Certificate.
You then have to create a device certificate signed by the CA certificate and register it with AWS IoT, which you can do by following the steps on Creating a Device Certificate Using Your CA Certificate. Save the certificate and the corresponding key pair; you will use them when you request a security token later. Also, remember the password you provide when you create the certificate.
Run the following command in the AWS CLI to attach the device certificate to your thing so that you can use thing attributes in policy variables.
If the attach-thing-principal command succeeds, the output is empty.
Next, configure an IAM role in your AWS account that will be assumed by the credentials provider on behalf of your device. You are required to associate two policies with the role: a trust policy that controls who can assume the role, and an access policy that controls which actions can be performed on which resources by assuming the role.
The following trust policy grants the credentials provider permission to assume the role. Put it in a text document and save the document with the name, trustpolicyforiot.json.
Run the following command in the AWS CLI to create an IAM role with the preceding trust policy.
The following is sample output of the create-role command.
The following access policy allows DynamoDB operations on the table that has the same name as the thing name that you created in Step 1, MyHomeThermostat, by using credentials-iot:ThingName as a policy variable. I explain after Step 5 about using thing attributes as policy variables. Put the following policy in a text document and save the document with the name, accesspolicyfordynamodb.json.
Run the following command in the AWS CLI to create the access policy.
The following is sample output of the create-policy command.
Finally, run the following command in the AWS CLI to attach the access policy to your role.
If the attach-role-policy command succeeds, the output is empty.
The IAM role that you have created must be passed to AWS IoT to create a role alias, as described in Step 4. The user who performs the operation requires iam:PassRole permission to authorize this action. You also should add permission for the iam:GetRole action to allow the user to retrieve information about the specified role. Create the following policy to grant iam:PassRole and iam:GetRole permissions. Name this policy, passrolepermission.json.
Run the following command in the AWS CLI to create the policy in your AWS account.
The following is sample output of the create-policy command.
Now, run the following command to attach the policy to the user.
If the attach-user-policy command succeeds, the output is empty.
Now that you have configured the IAM role, you will create a role alias with AWS IoT. You must provide the following pieces of information when creating a role alias:
RoleAlias: This is the primary key of the role alias data model and hence a mandatory attribute. It is a string; the minimum length is 1 character, and the maximum length is 128 characters.RoleArn: This is the Amazon Resource Name (ARN) of the IAM role you have created. This is also a mandatory attribute.CredentialDurationSeconds: This is an optional attribute specifying the validity (in seconds) of the security token. The minimum value is 900 seconds (15 minutes), and the maximum value is 3,600 seconds (60 minutes); the default value is 3,600 seconds, if not specified.Run the following command in the AWS CLI to create a role alias. Use the credentials of the user to whom you have given the iam:PassRole permission.
The following is sample output of the create-role-alias command. It contains the roleAlias as specified in the request and the roleArn.
You created and registered a certificate with AWS IoT earlier for successful authentication of your device. Now, you need to create and attach a policy to the certificate to authorize the request for the security token.
Let’s say you want to allow a thing to get credentials for the role alias, Thermostat-dynamodb-access-role-alias, with thing owner Alice, thing type thermostat, and the thing attached to a principal. The following policy, with thing attributes as policy variables, achieves these requirements. After this step, I explain more about using thing attributes as policy variables. Put the policy in a text document, and save it with the name, alicethermostatpolicy.json.
Run the following command in the AWS CLI to create the policy in your AWS IoT database.
The following is sample output of the create-policy command. It contains the policyName, policyArn, policyDocument, and policyVersionId.
Use the following command to attach the policy with the certificate you registered earlier.
If the attach-policy command succeeds, the output is empty.
You have completed all the necessary steps to request an AWS security token from the credentials provider!
Before I show how to request a security token, I want to explain more about how to use thing attributes as policy variables and the advantage of using them. As a prerequisite, a device must provide a thing name in the credentials provider request.
AWS IoT Simplified Permission Management allows you to associate a connection with a specific thing, and allow the thing name, thing type, and other thing attributes to be available as substitution variables in AWS IoT policies. You can write a generic AWS IoT policy as in alicethermostatpolicy.json in Step 5, attach it to multiple certificates, and authorize the connection as a thing. For example, you could attach alicethermostatpolicy.json to certificates corresponding to each of the thermostats you have that you want to assume the role alias, Thermostat-dynamodb-access-role-alias, and allow operations only on the table with the name that matches the thing name. For more information, see the full list of thing policy variables.
You also can use the following three substitution variables in the IAM role’s access policy (I used credentials-iot:ThingName in accesspolicyfordynamodb.json in Step 3):
credentials-iot:ThingNamecredentials-iot:ThingTypeNamecredentials-iot:AwsCertificateIdWhen the device provides the thing name in the request, the credentials provider fetches these three variables from the database and adds them as context variables to the security token. When the device uses the token to access DynamoDB, the variables in the role’s access policy are replaced with the corresponding values in the security token. Note that you also can use credentials-iot:AwsCertificateId as a policy variable; AWS IoT returns certificateId during registration.
Make an HTTPS request to the credentials provider to fetch a security token. You have to supply the following information:
Thermostat-dynamodb-access-role-alias) to be assumed in the request.x-amzn-iot-thingname. Note that the thing name is mandatory only if you have thing attributes as policy variables in AWS IoT or IAM policies.Run the following command in the AWS CLI to obtain your AWS account-specific endpoint for the credentials provider. See the DescribeEndpoint API documentation for further details.
The following is sample output of the describe-endpoint command. It contains the endpointAddress.
Note that if you are on Mac OS X, you need to export your certificate to a .pfx or .p12 file before you can pass it in the https request. Use OpenSSL with the following command to convert the device certificate from .pem to .pfx format. Remember the password because you will need it subsequently in a curl command.
Now, make an HTTPS request to the credentials provider to fetch a security token. You may use your preferred HTTP client for the request. I use curl in the following examples.
This command returns a security token object that has an accessKeyId, a secretAccessKey, a sessionToken, and an expiration. The following is sample output of the curl command.
Create a DynamoDB table called MyHomeThermostat in your AWS account. You will have to choose the hash (partition key) and the range (sort key) while creating the table to uniquely identify a record. Make the hash the serial_number of the thermostat and the range the timestamp of the record. Create a text file with the following JSON to put a temperature and humidity record in the table. Name the file, item.json.
You can use the accessKeyId, secretAccessKey, and sessionToken retrieved from the output of the curl command to sign a request that writes the temperature and humidity record to the DynamoDB table. Use the following commands to accomplish this.
The following is sample output of the put-item command.
In this blog post, I demonstrated how to retrieve a security token by using an X.509 certificate and then writing an item to a DynamoDB table by using the security token. Similarly, you could run applications on surveillance cameras or sensor devices that exchange the X.509 certificate for an AWS security token and use the token to upload video streams to Amazon Kinesis or telemetry data to Amazon CloudWatch.
If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, start a new thread on the AWS IoT forum.
– Ramkishore
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/lifting_a_finge.html
Police in the UK were able to read a fingerprint from a photo of a hand:
Staff from the unit’s specialist imaging team were able to enhance a picture of a hand holding a number of tablets, which was taken from a mobile phone, before fingerprint experts were able to positively identify that the hand was that of Elliott Morris.
[…]
Speaking about the pioneering techniques used in the case, Dave Thomas, forensic operations manager at the Scientific Support Unit, added: “Specialist staff within the JSIU fully utilised their expert image-enhancing skills which enabled them to provide something that the unit’s fingerprint identification experts could work. Despite being provided with only a very small section of the fingerprint which was visible in the photograph, the team were able to successfully identify the individual.”
Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/solar-powered-nature-camera/
Spring has sprung, and with it, sleepy-eyed wildlife is beginning to roam our gardens and local woodlands. So why not follow hackster.io maker reichley’s tutorial and build your own solar-powered squirrelhouse nature cam?
“I live half a mile above sea level and am SURROUNDED by animals…bears, foxes, turkeys, deer, squirrels, birds”, reichley explains in his tutorial. “Spring has arrived, and there are LOADS of squirrels running around. I was in the building mood and, being a nerd, wished to combine a common woodworking project with the connectivity and observability provided by single-board computers (and their camera add-ons).”
reichley started by sketching out a design for the house to determine where the various components would fit.
Since he’s fan of autonomy and renewable energy, he decided to run the project’s Raspberry Pi Zero W via solar power. To do so, he reiterated the design to include the necessary tech, scaling the roof to fit the panels.
To keep the project running 24/7, reichley had to figure out the overall power consumption of both the Zero W and the Raspberry Pi Camera Module, factoring in the constant WiFi connection and the sunshine hours in his garden.
He used a LiPo SHIM to bump up the power to the required 5V for the Zero. Moreover, he added a BH1750 lux sensor to shut off the LiPo SHIM, and thus the Pi, whenever it’s too dark for decent video.
To control the project, he used Calin Crisan’s motionEyeOS video surveillance operating system for single-board computers.
To build your own version, follow reichley’s tutorial, in which you can also find links to all the necessary code and components. You can also check out our free tutorial for building an infrared bird box using the Raspberry Pi NoIR Camera Module. As Eben said in our YouTube live Q&A last week, we really like nature cameras here at Pi Towers, and we’d love to see yours. So if you have any live-stream links or photography from your Raspberry Pi–powered nature cam, please share them with us!
The post Build a solar-powered nature camera for your garden appeared first on Raspberry Pi.
Post Syndicated from Ahin Thomas original https://www.backblaze.com/blog/b2-snapshot-return-refund-program/
Backblaze’s mission is making cloud storage astonishingly easy and affordable. That guides our focus — making our customers’ data more usable. Today, we’re pleased to introduce a trial of the B2 Snapshot Return Refund program. B2 customers have long been able to create a Snapshot of their data and order a hard drive with that data sent via FedEx anywhere in the world. Starting today, if the customer sends the drive back to Backblaze within 30 days, they will get a full refund. This new feature is available automatically for B2 customers when they order a Snapshot. There are no extra buttons to push or boxes to check — just send back the drive within 30 days and we’ll refund your money. To put it simply, we are offering the cloud storage industry’s only refundable rapid data egress service.
Last week, we cut the price of B2 downloads in half — from 2¢ per GB to 1¢ per GB. That 50% reduction makes B2’s download price 1/5 that of Amazon’s S3 (with B2 storage pricing already 1/4 that of S3). The price reduction and today’s introduction of the B2 Snapshot Return Refund program are deliberate moves to eliminate the industry’s biggest barrier to entry — the cost of using data stored in the cloud. Storage vendors who make it expensive to restore, or place time lag impediments to access, are reducing the usefulness of your data. We believe this is antithetical to encouraging the use of the cloud in the first place.
Our Computer Backup product already has a Restore Return Refund program. It’s incredibly popular, and we enjoy the almost daily “you just saved my bacon” letters that come back with the returned hard drives. Our customer surveys have repeatedly demonstrated that the ability to get data back is one of the things that has made our Computer Backup service one of the most popular in the industry. So, it made sense to us that our B2 customers could use a similar program.
There are many ways B2 customers can benefit from using the B2 Snapshot Return Refund program, here is a typical scenario.
Businesses in the Media and Entertainment (M&E) industry tend to have large quantities of digital media, and the amount of data will continue to increase in the coming years with more 4K and 8K cameras coming into regular use. When an organization needs to deliver or share that data, they typically have to manually download data from their internal storage system, and copy it on a thumb drive or hard drive, or perhaps create an LTO tape. Once that is done, they take their storage device, label it, and mail to their customer. Not only is this practice costly, time consuming, and potentially insecure, it doesn’t scale well with larger amounts of data.
With just a few clicks, you can easily distribute or share your digital media if it stored in the B2 Cloud. Here’s how the process works:
You can always keep the hard drive or flash drive and Backblaze, of course, will keep your money.
Each drive containing a Snapshot is encrypted. The encryption key can be found in your Backblaze B2 account after you log in. The FedEX tracking number is there as well. When the hard drive arrives at its destination you can provide the encryption key to the recipient and they’ll be able to access the files. Note that the encryption key must be entered each time the hard drive is started, so the data remains protected even if the hard drive is returned to Backblaze.
The B2 Snapshot Return Refund program supports Snapshots as large as 3.5 terabytes. That means you can send about 50 hours of 4k video to a client or partner by selecting the hard drive option. If you select the flash drive option, a Snapshot can be up to 110 gigabytes, which is about 1hr and 45 min of 4k video.
While the example uses an M&E workflow, any workflow requiring the exchange or distribution of large amounts of data across distinct geographies will benefit from this service.
Backblaze fully intends to offer the B2 Snapshot Return Refund Program for a long time. That said, there is no program like this in the industry and so we want to put some guardrails on it to ensure we can offer a sustainable program for all. Thus, the “fine print”:
Our industry has a habit of charging little to store data and then usurious amounts to get it back. There are certainly real costs involved in data retrieval. We outlined them in our post on the Cost of Cloud Storage. The industry rates charged for data retrieval are clearly strategic moves to try and lock customers in. To us, that runs counter to trying to do our part to make data useful and our customers’ lives easier. That viewpoint drives our efforts behind lowering our download pricing and the creation of this program.
We hope you enjoy the B2 Snapshot Return Refund program. If you have a moment, please tell us in the comments below how you might use it!
The post Introducing the B2 Snapshot Return Refund Program appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.
Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/factors-for-choosing-data-center/
This is part two of a series on the factors that an organization needs to consider when opening a data center and the challenges that must be met in the process.
In Part 1 of this series, we looked at the different types of data centers, the importance of location in planning a data center, data center certification, and the single most expensive factor in running a data center, power.
In Part 2, we continue to look at factors that need to considered both by those interested in a dedicated data center and those seeking to colocate in an existing center.
In part 1, we began our discussion of the power requirements of data centers.
As we discussed, redundancy and failover is a chief requirement for data center power. A redundantly designed power supply system is also a necessity for maintenance, as it enables repairs to be performed on one network, for example, without having to turn off servers, databases, or electrical equipment.
The common critical components of a data center’s power flow are:
Utility Supply is the power that comes from one or more utility grids. While most of us consider the grid to be our primary power supply (hats off to those of you who manage to live off the grid), politics, economics, and distribution make utility supply power susceptible to outages, which is why data centers must have autonomous power available to maintain availability.
Generators are used to supply power when the utility supply is unavailable. They convert mechanical energy, usually from motors, to electrical energy.
Transfer Switches are used to transfer electric load from one source or electrical device to another, such as from one utility line to another, from a generator to a utility, or between generators. The transfer could be manually activated or automatic to ensure continuous electrical power.
Distribution Panels get the power where it needs to go, taking a power feed and dividing it into separate circuits to supply multiple loads.
A UPS, as we touched on earlier, ensures that continuous power is available even when the main power source isn’t. It often consists of batteries that can come online almost instantaneously when the current power ceases. The power from a UPS does not have to last a long time as it is considered an emergency measure until the main power source can be restored. Another function of the UPS is to filter and stabilize the power from the main power supply.
Data center UPSs
PDU stands for the Power Distribution Unit and is the device that distributes power to the individual pieces of equipment.
After power, the networking connections to the data center are of prime importance. Can the data center obtain and maintain high-speed networking connections to the building? With networking, as with all aspects of a data center, availability is a primary consideration. Data center designers think of all possible ways service can be interrupted or lost, even briefly. Details such as the vulnerabilities in the route the network connections make from the core network (the backhaul) to the center, and where network connections enter and exit a building, must be taken into consideration in network and data center design.
Routers and switches are used to transport traffic between the servers in the data center and the core network. Just as with power, network redundancy is a prime factor in maintaining availability of data center services. Two or more upstream service providers are required to ensure that availability.
How fast a customer can transfer data to a data center is affected by: 1) the speed of the connections the data center has with the outside world, 2) the quality of the connections between the customer and the data center, and 3) the distance of the route from customer to the data center. The longer the length of the route and the greater the number of packets that must be transferred, the more significant a factor will be played by latency in the data transfer. Latency is the delay before a transfer of data begins following an instruction for its transfer. Generally latency, not speed, will be the most significant factor in transferring data to and from a data center. Packets transferred using the TCP/IP protocol suite, which is the conceptual model and set of communications protocols used on the internet and similar computer networks, must be acknowledged when received (ACK’d) and requires a communications roundtrip for each packet. If the data is in larger packets, the number of ACKs required is reduced, so latency will be a smaller factor in the overall network communications speed.
Latency generally will be less significant for data storage transfers than for cloud computing. Optimizations such as multi-threading, which is used in Backblaze’s Cloud Backup service, will generally improve overall transfer throughput if sufficient bandwidth is available.
Data center telecommunications equipment
Data center under floor cable runs
Computer, networking, and power generation equipment generates heat, and there are a number of solutions employed to rid a data center of that heat. The location and climate of the data center is of great importance to the data center designer because the climatic conditions dictate to a large degree what cooling technologies should be deployed that in turn affect the power used and the cost of using that power. The power required and cost needed to manage a data center in a warm, humid climate will vary greatly from managing one in a cool, dry climate. Innovation is strong in this area and many new approaches to efficient and cost-effective cooling are used in the latest data centers.
Switch’s uninterruptible, multi-system, HVAC Data Center Cooling Units
There are three primary ways data center cooling can be achieved:
Room Cooling cools the entire operating area of the data center. This method can be suitable for small data centers, but becomes more difficult and inefficient as IT equipment density and center size increase.
Row Cooling concentrates on cooling a data center on a row by row basis. In its simplest form, hot aisle/cold aisle data center design involves lining up server racks in alternating rows with cold air intakes facing one way and hot air exhausts facing the other. The rows composed of rack fronts are called cold aisles. Typically, cold aisles face air conditioner output ducts. The rows the heated exhausts pour into are called hot aisles. Typically, hot aisles face air conditioner return ducts.
Rack Cooling tackles cooling on a rack by rack basis. Air-conditioning units are dedicated to specific racks. This approach allows for maximum densities to be deployed per rack. This works best in data centers with fully loaded racks, otherwise there would be too much cooling capacity, and the air-conditioning losses alone could exceed the total IT load.
Data Centers are high-security facilities as they house business, government, and other data that contains personal, financial, and other secure information about businesses and individuals.
This list contains the physical-security considerations when opening or co-locating in a data center:
Layered Security Zones. Systems and processes are deployed to allow only authorized personnel in certain areas of the data center. Examples include keycard access, alarm systems, mantraps, secure doors, and staffed checkpoints.
Physical Barriers. Physical barriers, fencing and reinforced walls are used to protect facilities. In a colocation facility, one customers’ racks and servers are often inaccessible to other customers colocating in the same data center.
Backblaze racks secured in the data center
Monitoring Systems. Advanced surveillance technology monitors and records activity on approaching driveways, building entrances, exits, loading areas, and equipment areas. These systems also can be used to monitor and detect fire and water emergencies, providing early detection and notification before significant damage results.
Top-tier providers evaluate their data center security and facilities on an ongoing basis. Technology becomes outdated quickly, so providers must stay-on-top of new approaches and technologies in order to protect valuable IT assets.
To pass into high security areas of a data center requires passing through a security checkpoint where credentials are verified.
The gauntlet of cameras and steel bars one must pass before entering this data center
Data center colocation providers often differentiate themselves by offering value-added services. In addition to the required space, power, cooling, connectivity and security capabilities, the best solutions provide several on-site amenities. These accommodations include offices and workstations, conference rooms, and access to phones, copy machines, and office equipment.
Additional features may consist of kitchen facilities, break rooms and relaxation lounges, storage facilities for client equipment, and secure loading docks and freight elevators.
Moving into a data center is a major job for any organization. We wrote a post last year, Desert To Data in 7 Days — Our New Phoenix Data Center, about what it was like to move into our new data center in Phoenix, Arizona.
Our Director of Product Marketing Andy Klein wrote a popular post last year on what it’s like to visit a data center called A Day in the Life of a Data Center.
That’s it for part 2 of this series. If readers are interested, we could write a post about some of the new technologies and trends affecting data center design and use. Please let us know in the comments.
Here’s a tip on finding all the posts tagged with data center on our blog. Just follow https://www.backblaze.com/blog/tag/data-center/.
The post The Challenges of Opening a Data Center — Part 2 appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.
Post Syndicated from Chad Schmutzer original https://aws.amazon.com/blogs/compute/building-an-immersive-vr-streaming-solution-on-aws/
This post was contributed by:
Konstantin Wilms |
Shawn Przybilla |
Chad Schmutzer |
With the explosion in virtual reality (VR) technologies over the past few years, we’ve had an increasing number of customers ask us for advice and best practices around deploying their VR-based products and service offerings on the AWS Cloud. It soon became apparent that while the VR ecosystem is large in both scope and depth of types of workloads (gaming, e-medicine, security analytics, live streaming events, etc.), many of the workloads followed repeatable patterns, with storage and delivery of live and on-demand immersive video at the top of the list.
Looking at consumer trends, the desire for live and on-demand immersive video is fairly self-explanatory. VR has ushered in convenient and low-cost access for consumers and businesses to a wide variety of options for consuming content, ranging from browser playback of live and on-demand 360º video, all the way up to positional tracking systems with a high degree of immersion. All of these scenarios contain one lowest common denominator: video.
Which brings us to the topic of this post. We set out to build a solution that could support both live and on-demand events, bring with it a high degree of scalability, be flexible enough to support transformation of video if required, run at a low cost, and use open-source software to every extent possible.
In this post, we describe the reference architecture we created to solve this challenge, using Amazon EC2 Spot Instances, Amazon S3, Elastic Load Balancing, Amazon CloudFront, AWS CloudFormation, and Amazon CloudWatch, with open-source software such as NGINX, FFMPEG, and JavaScript-based client-side playback technologies. We step you through deployment of the solution and how the components work, as well as the capture, processing, and playback of the underlying live and on-demand immersive media streams.
This GitHub repository includes the source code necessary to follow along. We’ve also provided a self-paced workshop, from AWS re:Invent 2017 that breaks down this architecture even further. If you experience any issues or would like to suggest an enhancement, please use the GitHub issue tracker.
As a side note, you’ll also need a few additional components to take best advantage of the infrastructure:
You’re going to generate HTML5-compatible video (Apple HLS to be exact), but there are many other native iOS and Android options for consuming the media that you create. It’s also worth noting that your playback device should support projection of your input stream. We’ll talk more about that in the next section.
At its core, any flavor of media, be that audio or video, can be viewed with some level of immersion. The ability to interact passively or actively with the content brings with it a further level of immersion. When you look at VR devices with rotational and positional tracking, you naturally need more than an ability to interact with a flat plane of video. The challenge for any creative thus becomes a tradeoff between immersion features (degrees of freedom, monoscopic 2D or stereoscopic 3D, resolution, framerate) and overall complexity.
Where can you start from a simple and effective point of view, that enables you to build out a fairly modular solution and test it? There are a few areas we chose to be prescriptive with our solution.
Source capture from the Ricoh Theta S
First, monoscopic 360-degree video is currently one of the most commonly consumed formats on consumer devices. We explicitly chose to focus on this format, although the infrastructure is not limited to it. More on this later.
Second, if you look at most consumer-level cameras that provide live streaming ability, and even many professional rigs, there are at least two lenses or cameras at a minimum. The figure above illustrates a single capture from a Ricoh Theta S in monoscopic 2D. The left image captures 180 degrees of the field of view, and the right image captures the other 180 degrees.
For this post, we chose a typical midlevel camera (the Ricoh Theta S), and used a laptop with open-source software (Open Broadcaster Software) to encode and stream the content. Again, the solution infrastructure is not limited to this particular brand of camera. Any camera or encoder that outputs 360º video and encodes to H264+AAC with an RTMP transport will work.
Third, capturing and streaming multiple camera feeds brings additional requirements around stream synchronization and cost of infrastructure. There is also a requirement to stitch media in real time, which can be CPU and GPU-intensive. Many devices and platforms do this either on the device, or via outboard processing that sits close to the camera location. If you stitch and deliver a single stream, you can save the costs of infrastructure and bitrate/connectivity requirements. We chose to keep these aspects on the encoder side to save on cost and reduce infrastructure complexity.
Last, the most common delivery format that requires little to no processing on the infrastructure side is equirectangular projection, as per the above figure. By stitching and unwrapping the spherical coordinates into a flat plane, you can easily deliver the video exactly as you would with any other live or on-demand stream. The only caveat is that resolution and bit rate are of utmost importance. The higher you can push these (high bit rate @ 4K resolution), the more immersive the experience is for viewers. This is due to the increase in sharpness and reduction of compression artifacts.
Knowing that we would be transcoding potentially at 4K on the source camera, but in a format that could be transmuxed without an encoding penalty on the origin servers, we implemented a pass-through for the highest bit rate, and elected to only transcode lower bitrates. This requires some level of configuration on the source encoder, but saves on cost and infrastructure. Because you can conform the source stream, you may as well take advantage of that!
For this post, we chose not to focus on ways to optimize projection. However, the reference architecture does support this with additional open source components compiled into the FFMPEG toolchain. A number of options are available to this end, such as open source equirectangular to cubic transformation filters. There is a tradeoff, however, in that reprojection implies that all streams must be transcoded.
To get started, we’ve provided a CloudFormation template that you can launch directly into your own AWS account. We quickly review how it works, the solution’s components, key features, processing steps, and examine the main configuration files. Following this, you launch the stack, and then proceed with camera and encoder setup.
Immersive streaming reference architecture
A camera captures content, and with the help of a contribution encoder, publishes a live stream in equirectangular format. The stream is encoded at a high bit rate (at least 2.5 Mbps, but typically 16+ Mbps for 4K) using H264 video and AAC audio compression codecs, and delivered to a primary origin via the RTMP protocol. Streams may transit over the internet or dedicated links to the origins. Typically, for live events in the field, internet or bonded cellular are the most widely used.
The encoder is typically configured to push the live stream to a primary URI, with the ability (depending on the source encoding software/hardware) to roll over to a backup publishing point origin if the primary fails. Because you run across multiple Availability Zones, this architecture could handle an entire zone outage with minor disruption to live events. The primary and backup origins handle the ingestion of the live stream as well as transcoding to H264+AAC-based adaptive bit rate sets. After transcode, they package the streams into HLS for delivery and create a master-level manifest that references all adaptive bit rates.
The edge cache fleet pulls segments and manifests from the active origin on demand, and supports failover from primary to backup if the primary origin fails. By adding this caching tier, you effectively separate the encoding backend tier from the cache tier that responds to client or CDN requests. In addition to origin protection, this separation allows you to independently monitor, configure, and scale these components.
Viewers can use the sample HTML5 player (or compatible desktop, iOS or Android application) to view the streams. Navigation in the 360-degree view is handled either natively via device-based gyroscope, positionally via more advanced devices such as a head mount display, or via mouse drag on the desktop. Adaptive bit rate is key here, as this allows you to target multiple device types, giving the player on each device the option of selecting an optimum stream based on network conditions or device profile.
When you deploy the CloudFormation template, all the architecture services referenced above are created and launched. This includes:
The template also provisions the underlying dependencies:
The edge cache fleet instances need some way to discover the primary and backup origin locations. You use elastic network interfaces and elastic IP addresses for this purpose.
As each component of the infrastructure is provisioned, software required to transcode and process the streams across the Spot Instances is automatically deployed. This includes NGiNX-RTMP for ingest of live streams, FFMPEG for transcoding, NGINX for serving, and helper scripts to handle various tasks (potential Spot Instance interruptions, queueing, moving content to S3). Metrics and logs are available through CloudWatch and you can manage the deployment using the CloudFormation console or AWS CLI.
Key features include:
You’re supporting both live and on-demand. On-demand content is created automatically when the encoder stops publishing to the origin.
Spot Instances are used exclusively for infrastructure to optimize cost and scale throughput.
To protect the origin servers, the midtier cache fleet pulls, caches, and delivers to downstream CDNs.
The Application Load Balancer endpoint allows CloudFront or any third-party CDN to source content from the edge fleet and, indirectly, the origin.
These three components form the core of the stream ingest, transcode, packaging, and delivery infrastructure, as well as the VOD-processing component for creating transcoded VOD content on-demand.
All infrastructure can be easily created and modified using CloudFormation.
To provide an end-to-end experience right away, we’ve included a test player page hosted as a static site on S3. This page uses A-Frame, a cross-platform, open-source framework for building VR experiences in the browser. Though A-Frame provides many features, it’s used here to render a sphere that acts as a 3D canvas for your live stream.
At this stage, and before we discuss processing, it is important to understand how the architecture operates with Spot Instances.
Spot Instances are spare compute capacity in the AWS Cloud available to you at steep discounts compared to On-Demand prices. Spot Instances enables you to optimize your costs on the AWS Cloud and scale your application’s throughput up to 10X for the same budget. By selecting Spot Instances, you can save up-to 90% on On-Demand prices. This allows you to greatly reduce the cost of running the solution because, outside of S3 for storage and CloudFront for delivery, this solution is almost entirely dependent on Spot Instances for infrastructure requirements.
We also know that customers running events look to deploy streaming infrastructure at the lowest price point, so it makes sense to take advantage of it wherever possible. A potential challenge when using Spot Instances for live streaming and on-demand processing is that you need to proactively deal with potential Spot Instance interruptions. How can you best deal with this?
First, the origin is deployed in a primary/backup deployment. If a Spot Instance interruption happens on the primary origin, you can fail over to the backup with a brief interruption. Should a potential interruption not be acceptable, then either Reserved Instances or On-Demand options (or a combination) can be used at this tier.
Second, the edge cache fleet runs a job (started automatically at system boot) that periodically queries the local instance metadata to detect if an interruption is scheduled to occur. Spot Instance Interruption Notices provide a two-minute warning of a pending interruption. If you poll every 5 seconds, you have almost 2 full minutes to detach from the Load Balancer and drain or stop any traffic directed to your instance.
Lastly, use an SQS queue when transcoding. If a transcode for a Spot Instance is interrupted, the stale item falls back into the SQS queue and is eventually re-surfaced into the processing pipeline. Only remove items from the queue after the transcoded files have been successfully moved to the destination S3 bucket.
As discussed in the previous sections, you pass through the video for the highest bit rate to save on having to increase the instance size to transcode the 4K or similar high bit rate or resolution content.
We’ve selected a handful of bitrates for the adaptive bit rate stack. You can customize any of these to suit the requirements for your event. The default ABR stack includes:
These can be modified by editing the /etc/nginx/rtmp.d/rtmp.conf NGINX configuration file on the origin or the CloudFormation template.
It’s important to understand where and how streams are transcoded. When the source high bit rate stream enters the primary or backup origin at the /live RTMP application entry point, it is recorded on stop and start of publishing. On completion, it is moved to S3 by a cleanup script, and a message is placed in your SQS queue for workers to use. These workers transcode the media and push it to a playout location bucket.
This solution uses Spot Fleet with automatic scaling to drive the fleet size. You can customize it based on CloudWatch metrics, such as simple utilization metrics to drive the size of the fleet. Why use Spot Instances for the transcode option instead of Amazon Elastic Transcoder? This allows you to implement reprojection of the input stream via FFMPEG filters in the future.
The origins handle all the heavy live streaming work. Edges only store and forward the segments and manifests, and provide scaling plus reduction of burden on the origin. This lets you customize the origin to the right compute capacity without having to rely on a ‘high watermark’ for compute sizing, thus saving additional costs.
Loopback is an important concept for the live origins. The incoming stream entering /live is transcoded by FFMPEG to multiple bit rates, which are streamed back to the same host via RTMP, on a secondary publishing point /show. The secondary publishing point is transparent to the user and encoder, but handles HLS segment generation and cleanup, and keeps a sliding window of live segments and constantly updating manifests.
Our solution provides two key points of configuration that can be used to customize the solution to accommodate ingest, recording, transcoding, and delivery, all controlled via origin and edge configuration files, which are described later. In addition, a number of job scripts run on the instances to provide hooks into Spot Instance interruption events and the VOD SQS-based processing queue.
The rtmp.conf excerpt below also shows additional parameters that can be customized, such as maximum recording file size in Kbytes, HLS Fragment length, and Playlist sizes. We’ve created these in accordance with general industry best practices to ensure the reliable streaming and delivery of your content.
rtmp {
server {
listen 1935;
chunk_size 4000;
application live {
live on;
record all;
record_path /var/lib/nginx/rec;
record_max_size 128000K;
exec_record_done /usr/local/bin/record-postprocess.sh $path $basename;
exec /usr/local/bin/ffmpeg <…parameters…>;
}
application show {
live on;
hls on;
...
hls_type live;
hls_fragment 10s;
hls_playlist_length 60s;
...
}
}
}This exposes a few URL endpoints for debugging and general status. In production, you would most likely turn these off:
You also control the TTLs, as previously discussed. It’s important to note here that you are setting TTLs explicitly at the origin, instead of in CloudFront’s distribution configuration. While both are valid, this approach allows you to reconfigure and restart the service on the fly without having to push changes through CloudFront. This is useful for debugging any caching or playback issues.
location /stat {
...
}
location /stat.xsl {
...
}
location /control {
...
}
# origin controlled ttls
location ~* /hls/.*\.ts$ {
...
add_header Cache-Control "max-age=900";
expires 900s;
}
location ~* /hls/.*\.m3u8$ {
...
add_header Cache-Control "max-age=5";
expires 5s;
}Here is a brief overview of the scripts we use to handle the events and process of the solution. We encourage you to take some time to review them.
For more details, see the Delivery and Playback section later in this post.
With the processing and origination infrastructure running, you need to configure your camera and encoder.
As discussed, we chose to use a Ricoh Theta S camera and Open Broadcaster Software (OBS) to stitch and deliver a stream into the infrastructure. Ricoh provides a free ‘blender’ driver, which allows you to transform, stitch, encode, and deliver both transformed equirectangular (used for this post) video as well as spherical (two camera) video. The Theta provides an easy way to get capturing for under $300, and OBS is a free and open-source software application for capturing and live streaming on a budget. It is quick, cheap, and enjoys wide use by the gaming community. OBS lowers the barrier to getting started with immersive streaming.
While the resolution and bit rate of the Theta may not be 4K, it still provides us with a way to test the functionality of the entire pipeline end to end, without having to invest in a more expensive camera rig. One could also use this type of model to target smaller events, which may involve mobile devices with smaller display profiles, such as phones and potentially smaller sized tablets.
Looking for a more professional solution? Nokia, GoPro, Samsung, and many others have options ranging from $500 to $50,000. This solution is based around the Theta S capabilities, but we’d encourage you to extend it to meet your specific needs.
If your device can support equirectangular RTMP, then it can deliver media through the reference architecture (dependent on instance sizing for higher bit rate sources, of course). If additional features are required such as camera stitching, mixing, or device bonding, we’d recommend exploring a commercial solution such as Teradek Sphere.
Teradek Rig (Teradek) |
Ricoh Theta (CNET) |
All cameras have varied PC connectivity support. We chose the Ricoh Theta S due to the real-time video connectivity that it provides through software drivers on macOS and PC. If you plan to purchase a camera to use with a PC, confirm that it supports real-time capabilities as a peripheral device.
Now that you have a camera, encoder, and AWS stack running, you can finally publish a live stream.
To start streaming with OBS, configure the source camera and set a publishing point. Use the RTMP application name /live on port 1935 to ingest into the primary origin’s Elastic IP address provided as the CloudFormation output: primaryOriginElasticIp.
You also need to choose a stream name or stream key in OBS. You can use any stream name, but keep the naming short and lowercase, and use only alphanumeric characters. This avoids any parsing issues on client-side player frameworks. There’s no publish point protection in your deployment, so any stream key works with the default NGiNX-RTMP configuration. For more information about stream keys, publishing point security, and extending the NGiNX-RTMP module, see the NGiNX-RTMP Wiki.
You should end up with a configuration similar to the following:
OBS Stream Settings
The Output settings dialog allows us to rescale the Video canvas and encode it for delivery to our AWS infrastructure. In the dialog below, we’ve set the Theta to encode at 5 Mbps in CBR mode using a preset optimized for low CPU utilization. We chose these settings in accordance with best practices for the stream pass-through at the origin for the initial incoming bit rate. You may notice that they largely match the FFMPEG encoding settings we use on the origin – namely constant bit rate, a single audio track, and x264 encoding with the ‘veryfast’ encoding profile.
OBS Output Settings
As you may have noticed, an on-demand component is included in the solution architecture. When talking to customers, one frequent request that we see is that they would like to record the incoming stream with as little effort as possible.
NGINX-RTMP’s recording directives provide an easy way to accomplish this. We record any newly published stream on stream start at the primary or backup origins, using the incoming source stream, which also happens to be the highest bit rate. When the encoder stops broadcasting, NGINX-RTMP executes an exec_record_done script – record-postprocess.sh (described in the Configuration section earlier), which ensures that the content is well-formed, and then moves it to an S3 ingest bucket for processing.
Transcoding of content to make it ready for VOD as adaptive bit rate is a multi-step pipeline. First, Spot Instances in the transcoding cluster periodically poll the SQS queue for new jobs. Items on the queue are pulled off on demand by processing instances, and transcoded via FFMPEG into adaptive bit rate HLS. This allows you to also extend FFMPEG using filters for cubic and other bitrate-optimizing 360-specific transforms. Finally, transcoded content is moved from the ingest bucket to an egress bucket, making them ready for playback via your CloudFront distribution.
Separate ingest and egress by bucket to provide hard security boundaries between source recordings (which are highest quality and unencrypted), and destination derivatives (which may be lower quality and potentially require encryption). Bucket separation also allows you to order and archive input and output content using different taxonomies, which is common when moving content from an asset management and archival pipeline (the ingest bucket) to a consumer-facing playback pipeline (the egress bucket, and any other attached infrastructure or services, such as CMS, Mobile applications, and so forth).
Because streams are pushed over the internet, there is always the chance that an interruption could occur in the network path, or even at the origin side of the equation (primary to backup roll-over). Both of these scenarios could result in malformed or partial recordings being created. For the best level of reliability, encoding should always be recorded locally on-site as a precaution to deal with potential stream interruptions.
With the camera turned on and OBS streaming to AWS, the final step is to play the live stream. We’ve primarily tested the prototype player on the latest Chrome and Firefox browsers on macOS, so your mileage may vary on different browsers or operating systems. For those looking to try the livestream on Google Cardboard, or similar headsets, native apps for iOS (VRPlayer) and Android exist that can play back HLS streams.
The prototype player is hosted in an S3 bucket and can be found from the CloudFormation output clientWebsiteUrl. It requires a stream URL provided as a query parameter ?url=<stream_url> to begin playback. This stream URL is determined by the RTMP stream configuration in OBS. For example, if OBS is publishing to rtmp://x.x.x.x:1935/live/foo, the resulting playback URL would be:
https://<cloudFrontDistribution>/hls/foo.m3u8
The combined player URL and playback URL results in a path like this one:
https://<clientWebsiteUrl>/?url=https://<cloudFrontDistribution>/hls/foo.m3u8
To assist in setup/debugging, we’ve provided a test source as part of the CloudFormation template. A color bar pattern with timecode and audio is being generated by FFmpeg running as an ECS task. Much like OBS, FFmpeg is streaming the test pattern to the primary origin over the RTMP protocol. The prototype player and test HLS stream can be accessed by opening the clientTestPatternUrl CloudFormation output link.
Test Stream Playback
In this post, we walked you through the design and implementation of a full end-to-end immersive streaming solution architecture. As you may have noticed, there are a number of areas this could expand into, and we intend to do this in follow-up posts around the topic of virtual reality media workloads in the cloud. We’ve identified a number of topics such as load testing, content protection, client-side metrics and analytics, and CI/CD infrastructure for 24/7 live streams. If you have any requests, please drop us a line.
We would like to extend extra-special thanks to Scott Malkie and Chad Neal for their help and contributions to this post and reference architecture.
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