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The Decision on Transparency

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/transparency-in-business/

Backblaze transparency

This post by Backblaze’s CEO and co-founder Gleb Budman is the seventh 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
  7. The Decision on Transparency

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

“Are you crazy?” “Why would you do that?!” “You shouldn’t share that!”

These are just a few of the common questions and comments we heard after posting some of the information we have shared over the years. So was it crazy? Misguided? Should you do it?

With that background I’d like to dig into the decision to become so transparent, from releasing stats on hard drive failures, to storage pod specs, to publishing our cloud storage costs, and open sourcing the Reed-Solomon code. What was the thought process behind becoming so transparent when most companies work so hard to hide their inner workings, especially information such as the Storage Pod specs that would normally be considered a proprietary advantage? Most importantly I’d like to explore the positives and negatives of being so transparent.

Sharing Intellectual Property

The first “transparency” that garnered a flurry of “why would you share that?!” came as a result of us deciding to open source our Storage Pod design: publishing the specs, parts, prices, and how to build it yourself. The Storage Pod was a key component of our infrastructure, gave us a cost (and thus competitive) advantage, took significant effort to develop, and had a fair bit of intellectual property: the “IP.”

The negatives of sharing this are obvious: it allows our competitors to use the design to reduce our cost advantage, and it gives away the IP, which could be patentable or have value as a trade secret.

The positives were certainly less obvious, and at the time we couldn’t have guessed how massive they would be.

We wrestled with the decision: prospective users and others online didn’t believe we could offer our service for such a low price, thinking that we would burn through some cash hoard and then go out of business. We wanted to reassure them, but how?

This is how our response evolved:

We’ve built a lower cost storage platform.
But why would anyone believe us?
Because, we’ve designed our own servers and they’re less expensive.
But why would anyone believe they were so low cost and efficient?
Because here’s how much they cost versus others.
But why would anyone believe they cost that little and still enabled us to efficiently store data?
Because here are all the components they’re made of, this is how to build them, and this is how they work.
Ok, you can’t argue with that.

Great — so that would reassure people. But should we do this? Is it worth it?

This was 2009, we were a tiny company of seven people working from our co-founder’s one-bedroom apartment. We decided that the risk of not having potential customers trust us was more impactful than the risk of our competitors possibly deciding to use our server architecture. The former might kill the company in short order; the latter might make it harder for us to compete in the future. Moreover, we figured that most competitors were established on their own platforms and were unlikely to switch to ours, even if it were better.

Takeaway: Build your brand today. There are no assurances you will make it to tomorrow if you can’t make people believe in you today.

A Sharing Success Story — The Backblaze Storage Pod

So with that, we decided to publish everything about the Storage Pod. As for deciding to actually open source it? That was a ‘thank you’ to the open source community upon whose shoulders we stood as we used software such as Linux, Tomcat, etc.

With eight years of hindsight, here’s what happened:

As best as I can tell, none of our direct competitors ever used our Storage Pod design, opting instead to continue paying more for commercial solutions.

  • Hundreds of press articles have been written about Backblaze as a direct result of sharing the Storage Pod design.
  • Millions of people have read press articles or our blog posts about the Storage Pods.
  • Backblaze was established as a storage tech thought leader, and a resource for those looking for information in the space.
  • Our blog became viewed as a resource, not a corporate mouthpiece.
  • Recruiting has been made easier through the awareness of Backblaze, the appreciation for us taking on challenging tech problems in interesting ways, and for our openness.
  • Sourcing for our Storage Pods has become easier because we can point potential vendors to our blog posts and say, “here’s what we need.”

And those are just the direct benefits for us. One of the things that warms my heart is that doing this has helped others:

  • Several companies have started selling servers based on our Storage Pod designs.
  • Netflix credits Backblaze with being the inspiration behind their CDN servers.
  • Many schools, labs, and others have shared that they’ve been able to do what they didn’t think was possible because using our Storage Pod designs provided lower-cost storage.
  • And I want to believe that in general we pushed forward the development of low-cost storage servers in the industry.

So overall, the decision on being transparent and sharing our Storage Pod designs was a clear win.

Takeaway: Never underestimate the value of goodwill. It can help build new markets that fuel your future growth and create new ecosystems.

Sharing An “Almost Acquisition”

Acquisition announcements are par for the course. No company, however, talks about the acquisition that fell through. If rumors appear in the press, the company’s response is always, “no comment.” But in 2010, when Backblaze was almost, but not acquired, we wrote about it in detail. Crazy?

The negatives of sharing this are slightly less obvious, but the two issues most people worried about were, 1) the fact that the company could be acquired would spook customers, and 2) the fact that it wasn’t would signal to potential acquirers that something was wrong.

So, why share this at all? No one was asking “did you almost get acquired?”

First, we had established a culture of transparency and this was a significant event that occurred for us, thus we defaulted to assuming we would share. Second, we learned that acquisitions fall through all the time, not just during the early fishing stage, but even after term sheets are signed, diligence is done, and all the paperwork is complete. I felt we had learned some things about the process that would be valuable to others that were going through it.

As it turned out, we received emails from startup founders saying they saved the post for the future, and from lawyers, VCs, and advisors saying they shared them with their portfolio companies. Among the most touching emails I received was from a founder who said that after an acquisition fell through she felt so alone that she became incredibly depressed, and that reading our post helped her see that this happens and that things could be OK after. Being transparent about almost getting acquired was worth it just to help that one founder.

And what about the concerns? As for spooking customers, maybe some were — but our sign-ups went up, not down, afterward. Any company can be acquired, and many of the world’s largest have been. That we were being both thoughtful about where to go with it, and open about it, I believe gave customers a sense that we would do the right thing if it happened. And as for signaling to potential acquirers? The ones I’ve spoken with all knew this happens regularly enough that it’s not a factor.

Takeaway: Being open and transparent is also a form of giving back to others.

Sharing Strategic Data

For years people have been desperate to know how reliable are hard drives. They could go to Amazon for individual reviews, but someone saying “this drive died for me” doesn’t provide statistical insight. Google published a study that showed annualized drive failure rates, but didn’t break down the results by manufacturer or model. Since Backblaze has deployed about 100,000 hard drives to store customer data, we have been able to collect a wealth of data on the reliability of the drives by make, model, and size. Was Backblaze the only one with this data? Of course not — Google, Amazon, Microsoft, and any other cloud-scale storage provider tracked it. Yet none would publish. Should Backblaze?

Again, starting with the main negatives: 1) sharing which drives we liked could increase demand for them, thus reducing availability or increasing prices, and 2) publishing the data might make the drive vendors unhappy with us, thereby making it difficult for us to buy drives.

But we felt that the largest drive purchasers (Amazon, Google, etc.) already had their own stats and would buy the drives they chose, and if individuals or smaller companies used our stats, they wouldn’t sufficiently move the overall market demand. Also, we hoped that the drive companies would see that we were being fair in our analysis and, if anything, would leverage our data to make drives even better.

Again, publishing the data resulted in tremendous value for Backblaze, with millions of people having read the analysis that we put out quarterly. Also, becoming known as the place to go for drive reliability information is a natural fit with being a backup and storage provider. In addition, in a twist from many people’s expectations, some of the drive companies actually started working closer with us, seeing that we could be a good source of data for them as feedback. We’ve also seen many individuals and companies make more data-based decisions on which drives to buy, and researchers have used the data for a variety of analyses.

traffic spike from hard drive reliability post

Backblaze blog analytics showing spike in readership after a hard drive stats post

Takeaway: Being open and transparent is rarely as risky as it seems.

Sharing Revenue (And Other Metrics)

Journalists always want to publish company revenue and other metrics, and private companies always shy away from sharing. For a long time we did, too. Then, we opened up about that, as well.

The negatives of sharing these numbers are: 1) external parties may otherwise perceive you’re doing better than you are, 2) if you share numbers often, you may show that growth has slowed or worse, 3) it gives your competitors info to compare their own business too.

We decided that, while some may have perceived we were bigger, our scale was plenty significant. Since we choose what we share and when, it’s up to us whether to disclose at any point. And if our competitors compare, what will they actually change that would affect us?

I did wait to share revenue until I felt I had the right person to write about it. At one point a journalist said she wouldn’t write about us unless I disclosed revenue. I suggested we had a lot to offer for the story, but didn’t want to share revenue yet. She refused to budge and I walked away from the article. Several year later, I reached out to a journalist who had covered Backblaze before and I felt understood our business and offered to share revenue with him. He wrote a deep-dive about the company, with revenue being one of the components of the story.

Sharing these metrics showed that we were at scale and running a real business, one with positive unit economics and margins, but not one where we were gouging customers.

Takeaway: Being open with the press about items typically not shared can be uncomfortable, but the press can amplify your story.

Should You Share?

For Backblaze, I believe the results of transparency have been staggering. However, it’s not for everyone. Apple has, clearly, been wildly successful taking secrecy to the extreme. In their case, early disclosure combined with the long cycle of hardware releases could significantly impact sales of current products.

“For Backblaze, I believe the results of transparency have been staggering.” — Gleb Budman

I will argue, however, that for most startups transparency wins. Most startups need to establish credibility and trust, build awareness and a fan base, show that they understand what their customers need and be useful to them, and show the soul and passion behind the company. Some startup companies try to buy these virtues with investor money, and sometimes amplifying your brand via paid marketing helps. But, authentic transparency can build awareness and trust not only less expensively, but more deeply than money can buy.

Backblaze was open from the beginning. With no outside investors, as founders we were able to express ourselves and make our decisions. And it’s easier to be a company that shares if you do it from the start, but for any company, here are a few suggestions:

  1. Ask about sharing: If something significant happens — good or bad — ask “should we share this?” If you made a tough decision, ask “should we share the thinking behind the decision and why it was tough?”
  2. Default to yes: It’s often scary to share, but look for the reasons to say ‘yes,’ not the reasons to say ‘no.’ That doesn’t mean you won’t sometimes decide not to, but make that the high bar.
  3. Minimize reviews: Press releases tend to be sanitized and boring because they’ve been endlessly wordsmithed by committee. Establish the few things you don’t want shared, but minimize the number of people that have to see anything else before it can go out. Teach, then trust.
  4. Engage: Sharing will result in comments on your blog, social, articles, etc. Reply to people’s questions and engage. It’ll make the readers more engaged and give you a better understanding of what they’re looking for.
  5. Accept mistakes: Things will become public that aren’t perfectly sanitized. Accept that and don’t punish people for oversharing.

Building a culture of a company that is open to sharing takes time, but continuous practice will build that, and over time the company will navigate its voice and approach to sharing.

The post The Decision on Transparency appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Automating Security Group Updates with AWS Lambda

Post Syndicated from Ian Scofield original https://aws.amazon.com/blogs/compute/automating-security-group-updates-with-aws-lambda/

Customers often use public endpoints to perform cross-region replication or other application layer communication to remote regions. But a common problem is how do you protect these endpoints? It can be tempting to open up the security groups to the world due to the complexity of keeping security groups in sync across regions with a dynamically changing infrastructure.

Consider a situation where you are running large clusters of instances in different regions that all require internode connectivity. One approach would be to use a VPN tunnel between regions to provide a secure tunnel over which to send your traffic. A good example of this is the Transit VPC Solution, which is a published AWS solution to help customers quickly get up and running. However, this adds additional cost and complexity to your solution due to the newly required additional infrastructure.

Another approach, which I’ll explore in this post, is to restrict access to the nodes by whitelisting the public IP addresses of your hosts in the opposite region. Today, I’ll outline a solution that allows for cross-region security group updates, can handle remote region failures, and supports external actions such as manually terminating instances or adding instances to an existing Auto Scaling group.

Solution overview

The overview of this solution is diagrammed below. Although this post covers limiting access to your instances, you should still implement encryption to protect your data in transit.

If your entire infrastructure is running in a single region, you can reference a security group as the source, allowing your IP addresses to change without any updates required. However, if you’re going across the public internet between regions to perform things like application-level traffic or cross-region replication, this is no longer an option. Security groups are regional. When you go across regions it can be tempting to drop security to enable this communication.

Although using an Elastic IP address can provide you with a static IP address that you can define as a source for your security groups, this may not always be feasible, especially when automatic scaling is desired.

In this example scenario, you have a distributed database that requires full internode communication for replication. If you place a cluster in us-east-1 and us-west-2, you must provide a secure method of communication between the two. Because the database uses cloud best practices, you can add or remove nodes as the load varies.

To start the process of updating your security groups, you must know when an instance has come online to trigger your workflow. Auto Scaling groups have the concept of lifecycle hooks that enable you to perform custom actions as the group launches or terminates instances.

When Auto Scaling begins to launch or terminate an instance, it puts the instance into a wait state (Pending:Wait or Terminating:Wait). The instance remains in this state while you perform your various actions until either you tell Auto Scaling to Continue, Abandon, or the timeout period ends. A lifecycle hook can trigger a CloudWatch event, publish to an Amazon SNS topic, or send to an Amazon SQS queue. For this example, you use CloudWatch Events to trigger an AWS Lambda function that updates an Amazon DynamoDB table.

Component breakdown

Here’s a quick breakdown of the components involved in this solution:

• Lambda function
• CloudWatch event
• DynamoDB table

Lambda function

The Lambda function automatically updates your security groups, in the following way:

1. Determines whether a change was triggered by your Auto Scaling group lifecycle hook or manually invoked for a “true up” functionality, which I discuss later in this post.
2. Describes the instances in the Auto Scaling group and obtain public IP addresses for each instance.
3. Updates both local and remote DynamoDB tables.
4. Compares the list of public IP addresses for both local and remote clusters with what’s already in the local region security group. Update the security group.
5. Compares the list of public IP addresses for both local and remote clusters with what’s already in the remote region security group. Update the security group
6. Signals CONTINUE back to the lifecycle hook.

CloudWatch event

The CloudWatch event triggers when an instance passes through either the launching or terminating states. When the Lambda function gets invoked, it receives an event that looks like the following:

{
	"account": "123456789012",
	"region": "us-east-1",
	"detail": {
		"LifecycleHookName": "hook-launching",
		"AutoScalingGroupName": "",
		"LifecycleActionToken": "33965228-086a-4aeb-8c26-f82ed3bef495",
		"LifecycleTransition": "autoscaling:EC2_INSTANCE_LAUNCHING",
		"EC2InstanceId": "i-017425ec54f22f994"
	},
	"detail-type": "EC2 Instance-launch Lifecycle Action",
	"source": "aws.autoscaling",
	"version": "0",
	"time": "2017-05-03T02:20:59Z",
	"id": "cb930cf8-ce8b-4b6c-8011-af17966eb7e2",
	"resources": [
		"arn:aws:autoscaling:us-east-1:123456789012:autoScalingGroup:d3fe9d96-34d0-4c62-b9bb-293a41ba3765:autoScalingGroupName/"
	]
}

DynamoDB table

You use DynamoDB to store lists of remote IP addresses in a local table that is updated by the opposite region as a failsafe source of truth. Although you can describe your Auto Scaling group for the local region, you must maintain a list of IP addresses for the remote region.

To minimize the number of describe calls and prevent an issue in the remote region from blocking your local scaling actions, we keep a list of the remote IP addresses in a local DynamoDB table. Each Lambda function in each region is responsible for updating the public IP addresses of its Auto Scaling group for both the local and remote tables.

As with all the infrastructure in this solution, there is a DynamoDB table in both regions that mirror each other. For example, the following screenshot shows a sample DynamoDB table. The Lambda function in us-east-1 would update the DynamoDB entry for us-east-1 in both tables in both regions.

By updating a DynamoDB table in both regions, it allows the local region to gracefully handle issues with the remote region, which would otherwise prevent your ability to scale locally. If the remote region becomes inaccessible, you have a copy of the latest configuration from the table that you can use to continue to sync with your security groups. When the remote region comes back online, it pushes its updated public IP addresses to the DynamoDB table. The security group is updated to reflect the current status by the remote Lambda function.

 

Walkthrough

Note: All of the following steps are performed in both regions. The Launch Stack buttons will default to the us-east-1 region.

Here’s a quick overview of the steps involved in this process:

1. An instance is launched or terminated, which triggers an Auto Scaling group lifecycle hook, triggering the Lambda function via CloudWatch Events.
2. The Lambda function retrieves the list of public IP addresses for all instances in the local region Auto Scaling group.
3. The Lambda function updates the local and remote region DynamoDB tables with the public IP addresses just received for the local Auto Scaling group.
4. The Lambda function updates the local region security group with the public IP addresses, removing and adding to ensure that it mirrors what is present for the local and remote Auto Scaling groups.
5. The Lambda function updates the remote region security group with the public IP addresses, removing and adding to ensure that it mirrors what is present for the local and remote Auto Scaling groups.

Prerequisites

To deploy this solution, you need to have Auto Scaling groups, launch configurations, and a base security group in both regions. To expedite this process, this CloudFormation template can be launched in both regions.

Step 1: Launch the AWS SAM template in the first region

To make the deployment process easy, I’ve created an AWS Serverless Application Model (AWS SAM) template, which is a new specification that makes it easier to manage and deploy serverless applications on AWS. This template creates the following resources:

• A Lambda function, to perform the various security group actions
• A DynamoDB table, to track the state of the local and remote Auto Scaling groups
• Auto Scaling group lifecycle hooks for instance launching and terminating
• A CloudWatch event, to track the EC2 Instance-Launch Lifecycle-Action and EC2 Instance-terminate Lifecycle-Action events
• A pointer from the CloudWatch event to the Lambda function, and the necessary permissions

Download the template from here or click to launch.

Upon launching the template, you’ll be presented with a list of parameters which includes the remote/local names for your Auto Scaling Groups, AWS region, Security Group IDs, DynamoDB table names, as well as where the code for the Lambda function is located. Because this is the first region you’re launching the stack in, fill out all the parameters except for the RemoteTable parameter as it hasn’t been created yet (you fill this in later).

Step 2: Test the local region

After the stack has finished launching, you can test the local region. Open the EC2 console and find the Auto Scaling group that was created when launching the prerequisite stack. Change the desired number of instances from 0 to 1.

For both regions, check your security group to verify that the public IP address of the instance created is now in the security group.

Local region:

Remote region:

Now, change the desired number of instances for your group back to 0 and verify that the rules are properly removed.

Local region:

Remote region:

Step 3: Launch in the remote region

When you deploy a Lambda function using CloudFormation, the Lambda zip file needs to reside in the same region you are launching the template. Once you choose your remote region, create an Amazon S3 bucket and upload the Lambda zip file there. Next, go to the remote region and launch the same SAM template as before, but make sure you update the CodeBucket and CodeKey parameters. Also, because this is the second launch, you now have all the values and can fill out all the parameters, specifically the RemoteTable value.

 

Step 4: Update the local region Lambda environment variable

When you originally launched the template in the local region, you didn’t have the name of the DynamoDB table for the remote region, because you hadn’t created it yet. Now that you have launched the remote template, you can perform a CloudFormation stack update on the initial SAM template. This populates the remote DynamoDB table name into the initial Lambda function’s environment variables.

In the CloudFormation console in the initial region, select the stack. Under Actions, choose Update Stack, and select the SAM template used for both regions. Under Parameters, populate the remote DynamoDB table name, as shown below. Choose Next and let the stack update complete. This updates your Lambda function and completes the setup process.

 

Step 5: Final testing

You now have everything fully configured and in place to trigger security group changes based on instances being added or removed to your Auto Scaling groups in both regions. Test this by changing the desired capacity of your group in both regions.

True up functionality
If an instance is manually added or removed from the Auto Scaling group, the lifecycle hooks don’t get triggered. To account for this, the Lambda function supports a “true up” functionality in which the function can be manually invoked. If you paste in the following JSON text for your test event, it kicks off the entire workflow. For added peace of mind, you can also have this function fire via a CloudWatch event with a CRON expression for nearly continuous checking.

{
	"detail": {
		"AutoScalingGroupName": "<your ASG name>"
	},
	"trueup":true
}

Extra credit

Now that all the resources are created in both regions, go back and break down the policy to incorporate resource-level permissions for specific security groups, Auto Scaling groups, and the DynamoDB tables.

Although this post is centered around using public IP addresses for your instances, you could instead use a VPN between regions. In this case, you would still be able to use this solution to scope down the security groups to the cluster instances. However, the code would need to be modified to support private IP addresses.

 

Conclusion

At this point, you now have a mechanism in place that captures when a new instance is added to or removed from your cluster and updates the security groups in both regions. This ensures that you are locking down your infrastructure securely by allowing access only to other cluster members.

Keep in mind that this architecture (lifecycle hooks, CloudWatch event, Lambda function, and DynamoDB table) requires that the infrastructure to be deployed in both regions, to have synchronization going both ways.

Because this Lambda function is modifying security group rules, it’s important to have an audit log of what has been modified and who is modifying them. The out-of-the-box function provides logs in CloudWatch for what IP addresses are being added and removed for which ports. As these are all API calls being made, they are logged in CloudTrail and can be traced back to the IAM role that you created for your lifecycle hooks. This can provide historical data that can be used for troubleshooting or auditing purposes.

Security is paramount at AWS. We want to ensure that customers are protecting access to their resources. This solution helps you keep your security groups in both regions automatically in sync with your Auto Scaling group resources. Let us know if you have any questions or other solutions you’ve come up with!

Ben’s Raspberry Pi Twilight Zone pinball hack

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/twilight-zone-pinball-display/

When Ben North was faced with the dilemma of his nine-year-old son wanting him to watch his pinball games while, at the same time, Ben should be doing housework, he came up with a brilliant hack. Ben decided to investigate the inner workings of his twenty-year-old Twilight Zone pinball machine to convert its score display data into a video stream he could keep an eye on while working.

Ben North Raspberry Pi Twilight Zone Pinball

Ben ended up with this. Read on to find out how…

Dad? Dad! DAD!!

Kids love sharing their achievements. That’s a given. And so, after Ben introduced his son Zach to his beloved pinball machine, Zach wanted his dad to witness his progress. However, at some point Ben had to get back to the dull reality of adulting.

My son Zach, now 9, has been steadily getting better at [playing pinball], and is keen for me to watch his games. So he and I wanted a way for me to keep an eye on how his game is going, while I do other jobs elsewhere.

The two of them thought that, with the right tools and some fiddling, they could hijack the machine’s score information on its way to the dot matrix display and divert it to a computer. “One way to do this would be to set up a webcam.” Ben explains on his blog, “But where’s the fun in that?”

Twilight Zone pinball wizardry

After researching how the dot matrix receives and displays the score data, Ben and Zach figured out how to fetch its output using a 16-channel USB logic analyser. Then they dove into learning to convert the data the logic analyser outputs back into images.

Ben North Raspberry Pi Twilight Zone Pinball

“Exploring in more detail confirmed that the data looked reasonable. We could see well-distinguished frames and rows, and within each row, the pixel data had a mixture of high (lit pixel) and low (dark pixel).”

After Ben managed to convert the signals of one frame into a human-readable pixel image, it was time to think about the hardware that could do this conversion in real time. Though he and Zach were convinced they would have to build custom hardware to complete their project, they decided to first give the Raspberry Pi a go. And it turned out that the Pi was up to the challenge!

Ben North Raspberry Pi Twilight Zone Pinball - example output

“By an amazing coincidence, the [first] frame I decoded was one showing that I am the current Lost In The Zone champion.”

To decode the first frame, Ben had written a Python script. However, he coded the program to produce a score live stream in C++, since this language is better at handling high-speed input and output. To make sure Zach would learn from the experience, Ben explained the how and why of the program to him.

I talked through with Zach what the program needed to do — detect clock edges, sample pixel data, collect rows, etc. — but then he left me to do ‘all the boring typing’.

Ben used various pieces of open-source software while working on this project, including the sigrok suite for signal analysis and the multimedia framework gstreamer for handling the live video stream to the Raspberry Pi.

Find more information about the Twilight Zone pinball build, including a lot of technical details and the code itself, on Ben’s blog.

Worthy self-promotion from Ben

“I also did an FPGA project to replicate some of the Colossus code-breaking machine used in Bletchley Park during World War II,” explained Ben in our recent emails. “with a Raspberry Pi as the host.”

Colossus computer Twilight Zone Pinball

The original Colossus, not Ben’s.
Image c/o Wikipedia

As a bit of a history nerd myself, I think this is beyond cool. And if, like me, you’d like to learn more, check out the link here.

The post Ben’s Raspberry Pi Twilight Zone pinball hack appeared first on Raspberry Pi.

Using AWS Step Functions State Machines to Handle Workflow-Driven AWS CodePipeline Actions

Post Syndicated from Marcilio Mendonca original https://aws.amazon.com/blogs/devops/using-aws-step-functions-state-machines-to-handle-workflow-driven-aws-codepipeline-actions/

AWS CodePipeline is a continuous integration and continuous delivery service for fast and reliable application and infrastructure updates. It offers powerful integration with other AWS services, such as AWS CodeBuildAWS CodeDeployAWS CodeCommit, AWS CloudFormation and with third-party tools such as Jenkins and GitHub. These services make it possible for AWS customers to successfully automate various tasks, including infrastructure provisioning, blue/green deployments, serverless deployments, AMI baking, database provisioning, and release management.

Developers have been able to use CodePipeline to build sophisticated automation pipelines that often require a single CodePipeline action to perform multiple tasks, fork into different execution paths, and deal with asynchronous behavior. For example, to deploy a Lambda function, a CodePipeline action might first inspect the changes pushed to the code repository. If only the Lambda code has changed, the action can simply update the Lambda code package, create a new version, and point the Lambda alias to the new version. If the changes also affect infrastructure resources managed by AWS CloudFormation, the pipeline action might have to create a stack or update an existing one through the use of a change set. In addition, if an update is required, the pipeline action might enforce a safety policy to infrastructure resources that prevents the deletion and replacement of resources. You can do this by creating a change set and having the pipeline action inspect its changes before updating the stack. Change sets that do not conform to the policy are deleted.

This use case is a good illustration of workflow-driven pipeline actions. These are actions that run multiple tasks, deal with async behavior and loops, need to maintain and propagate state, and fork into different execution paths. Implementing workflow-driven actions directly in CodePipeline can lead to complex pipelines that are hard for developers to understand and maintain. Ideally, a pipeline action should perform a single task and delegate the complexity of dealing with workflow-driven behavior associated with that task to a state machine engine. This would make it possible for developers to build simpler, more intuitive pipelines and allow them to use state machine execution logs to visualize and troubleshoot their pipeline actions.

In this blog post, we discuss how AWS Step Functions state machines can be used to handle workflow-driven actions. We show how a CodePipeline action can trigger a Step Functions state machine and how the pipeline and the state machine are kept decoupled through a Lambda function. The advantages of using state machines include:

  • Simplified logic (complex tasks are broken into multiple smaller tasks).
  • Ease of handling asynchronous behavior (through state machine wait states).
  • Built-in support for choices and processing different execution paths (through state machine choices).
  • Built-in visualization and logging of the state machine execution.

The source code for the sample pipeline, pipeline actions, and state machine used in this post is available at https://github.com/awslabs/aws-codepipeline-stepfunctions.

Overview

This figure shows the components in the CodePipeline-Step Functions integration that will be described in this post. The pipeline contains two stages: a Source stage represented by a CodeCommit Git repository and a Prod stage with a single Deploy action that represents the workflow-driven action.

This action invokes a Lambda function (1) called the State Machine Trigger Lambda, which, in turn, triggers a Step Function state machine to process the request (2). The Lambda function sends a continuation token back to the pipeline (3) to continue its execution later and terminates. Seconds later, the pipeline invokes the Lambda function again (4), passing the continuation token received. The Lambda function checks the execution state of the state machine (5,6) and communicates the status to the pipeline. The process is repeated until the state machine execution is complete. Then the Lambda function notifies the pipeline that the corresponding pipeline action is complete (7). If the state machine has failed, the Lambda function will then fail the pipeline action and stop its execution (7). While running, the state machine triggers various Lambda functions to perform different tasks. The state machine and the pipeline are fully decoupled. Their interaction is handled by the Lambda function.

The Deploy State Machine

The sample state machine used in this post is a simplified version of the use case, with emphasis on infrastructure deployment. The state machine will follow distinct execution paths and thus have different outcomes, depending on:

  • The current state of the AWS CloudFormation stack.
  • The nature of the code changes made to the AWS CloudFormation template and pushed into the pipeline.

If the stack does not exist, it will be created. If the stack exists, a change set will be created and its resources inspected by the state machine. The inspection consists of parsing the change set results and detecting whether any resources will be deleted or replaced. If no resources are being deleted or replaced, the change set is allowed to be executed and the state machine completes successfully. Otherwise, the change set is deleted and the state machine completes execution with a failure as the terminal state.

Let’s dive into each of these execution paths.

Path 1: Create a Stack and Succeed Deployment

The Deploy state machine is shown here. It is triggered by the Lambda function using the following input parameters stored in an S3 bucket.

Create New Stack Execution Path

{
    "environmentName": "prod",
    "stackName": "sample-lambda-app",
    "templatePath": "infra/Lambda-template.yaml",
    "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
    "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ"
}

Note that some values used here are for the use case example only. Account-specific parameters like revisionS3Bucket and revisionS3Key will be different when you deploy this use case in your account.

These input parameters are used by various states in the state machine and passed to the corresponding Lambda functions to perform different tasks. For example, stackName is used to create a stack, check the status of stack creation, and create a change set. The environmentName represents the environment (for example, dev, test, prod) to which the code is being deployed. It is used to prefix the name of stacks and change sets.

With the exception of built-in states such as wait and choice, each state in the state machine invokes a specific Lambda function.  The results received from the Lambda invocations are appended to the state machine’s original input. When the state machine finishes its execution, several parameters will have been added to its original input.

The first stage in the state machine is “Check Stack Existence”. It checks whether a stack with the input name specified in the stackName input parameter already exists. The output of the state adds a Boolean value called doesStackExist to the original state machine input as follows:

{
  "doesStackExist": true,
  "environmentName": "prod",
  "stackName": "sample-lambda-app",
  "templatePath": "infra/lambda-template.yaml",
  "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
  "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ",
}

The following stage, “Does Stack Exist?”, is represented by Step Functions built-in choice state. It checks the value of doesStackExist to determine whether a new stack needs to be created (doesStackExist=true) or a change set needs to be created and inspected (doesStackExist=false).

If the stack does not exist, the states illustrated in green in the preceding figure are executed. This execution path creates the stack, waits until the stack is created, checks the status of the stack’s creation, and marks the deployment successful after the stack has been created. Except for “Stack Created?” and “Wait Stack Creation,” each of these stages invokes a Lambda function. “Stack Created?” and “Wait Stack Creation” are implemented by using the built-in choice state (to decide which path to follow) and the wait state (to wait a few seconds before proceeding), respectively. Each stage adds the results of their Lambda function executions to the initial input of the state machine, allowing future stages to process them.

Path 2: Safely Update a Stack and Mark Deployment as Successful

Safely Update a Stack and Mark Deployment as Successful Execution Path

If the stack indicated by the stackName parameter already exists, a different path is executed. (See the green states in the figure.) This path will create a change set and use wait and choice states to wait until the change set is created. Afterwards, a stage in the execution path will inspect  the resources affected before the change set is executed.

The inspection procedure represented by the “Inspect Change Set Changes” stage consists of parsing the resources affected by the change set and checking whether any of the existing resources are being deleted or replaced. The following is an excerpt of the algorithm, where changeSetChanges.Changes is the object representing the change set changes:

...
var RESOURCES_BEING_DELETED_OR_REPLACED = "RESOURCES-BEING-DELETED-OR-REPLACED";
var CAN_SAFELY_UPDATE_EXISTING_STACK = "CAN-SAFELY-UPDATE-EXISTING-STACK";
for (var i = 0; i < changeSetChanges.Changes.length; i++) {
    var change = changeSetChanges.Changes[i];
    if (change.Type == "Resource") {
        if (change.ResourceChange.Action == "Delete") {
            return RESOURCES_BEING_DELETED_OR_REPLACED;
        }
        if (change.ResourceChange.Action == "Modify") {
            if (change.ResourceChange.Replacement == "True") {
                return RESOURCES_BEING_DELETED_OR_REPLACED;
            }
        }
    }
}
return CAN_SAFELY_UPDATE_EXISTING_STACK;

The algorithm returns different values to indicate whether the change set can be safely executed (CAN_SAFELY_UPDATE_EXISTING_STACK or RESOURCES_BEING_DELETED_OR_REPLACED). This value is used later by the state machine to decide whether to execute the change set and update the stack or interrupt the deployment.

The output of the “Inspect Change Set” stage is shown here.

{
  "environmentName": "prod",
  "stackName": "sample-lambda-app",
  "templatePath": "infra/lambda-template.yaml",
  "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
  "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ",
  "doesStackExist": true,
  "changeSetName": "prod-sample-lambda-app-change-set-545",
  "changeSetCreationStatus": "complete",
  "changeSetAction": "CAN-SAFELY-UPDATE-EXISTING-STACK"
}

At this point, these parameters have been added to the state machine’s original input:

  • changeSetName, which is added by the “Create Change Set” state.
  • changeSetCreationStatus, which is added by the “Get Change Set Creation Status” state.
  • changeSetAction, which is added by the “Inspect Change Set Changes” state.

The “Safe to Update Infra?” step is a choice state (its JSON spec follows) that simply checks the value of the changeSetAction parameter. If the value is equal to “CAN-SAFELY-UPDATE-EXISTING-STACK“, meaning that no resources will be deleted or replaced, the step will execute the change set by proceeding to the “Execute Change Set” state. The deployment is successful (the state machine completes its execution successfully).

"Safe to Update Infra?": {
      "Type": "Choice",
      "Choices": [
        {
          "Variable": "$.taskParams.changeSetAction",
          "StringEquals": "CAN-SAFELY-UPDATE-EXISTING-STACK",
          "Next": "Execute Change Set"
        }
      ],
      "Default": "Deployment Failed"
 }

Path 3: Reject Stack Update and Fail Deployment

Reject Stack Update and Fail Deployment Execution Path

If the changeSetAction parameter is different from “CAN-SAFELY-UPDATE-EXISTING-STACK“, the state machine will interrupt the deployment by deleting the change set and proceeding to the “Deployment Fail” step, which is a built-in Fail state. (Its JSON spec follows.) This state causes the state machine to stop in a failed state and serves to indicate to the Lambda function that the pipeline deployment should be interrupted in a fail state as well.

 "Deployment Failed": {
      "Type": "Fail",
      "Cause": "Deployment Failed",
      "Error": "Deployment Failed"
    }

In all three scenarios, there’s a state machine’s visual representation available in the AWS Step Functions console that makes it very easy for developers to identify what tasks have been executed or why a deployment has failed. Developers can also inspect the inputs and outputs of each state and look at the state machine Lambda function’s logs for details. Meanwhile, the corresponding CodePipeline action remains very simple and intuitive for developers who only need to know whether the deployment was successful or failed.

The State Machine Trigger Lambda Function

The Trigger Lambda function is invoked directly by the Deploy action in CodePipeline. The CodePipeline action must pass a JSON structure to the trigger function through the UserParameters attribute, as follows:

{
  "s3Bucket": "codepipeline-StepFunctions-sample",
  "stateMachineFile": "state_machine_input.json"
}

The s3Bucket parameter specifies the S3 bucket location for the state machine input parameters file. The stateMachineFile parameter specifies the file holding the input parameters. By being able to specify different input parameters to the state machine, we make the Trigger Lambda function and the state machine reusable across environments. For example, the same state machine could be called from a test and prod pipeline action by specifying a different S3 bucket or state machine input file for each environment.

The Trigger Lambda function performs two main tasks: triggering the state machine and checking the execution state of the state machine. Its core logic is shown here:

exports.index = function (event, context, callback) {
    try {
        console.log("Event: " + JSON.stringify(event));
        console.log("Context: " + JSON.stringify(context));
        console.log("Environment Variables: " + JSON.stringify(process.env));
        if (Util.isContinuingPipelineTask(event)) {
            monitorStateMachineExecution(event, context, callback);
        }
        else {
            triggerStateMachine(event, context, callback);
        }
    }
    catch (err) {
        failure(Util.jobId(event), callback, context.invokeid, err.message);
    }
}

Util.isContinuingPipelineTask(event) is a utility function that checks if the Trigger Lambda function is being called for the first time (that is, no continuation token is passed by CodePipeline) or as a continuation of a previous call. In its first execution, the Lambda function will trigger the state machine and send a continuation token to CodePipeline that contains the state machine execution ARN. The state machine ARN is exposed to the Lambda function through a Lambda environment variable called stateMachineArn. Here is the code that triggers the state machine:

function triggerStateMachine(event, context, callback) {
    var stateMachineArn = process.env.stateMachineArn;
    var s3Bucket = Util.actionUserParameter(event, "s3Bucket");
    var stateMachineFile = Util.actionUserParameter(event, "stateMachineFile");
    getStateMachineInputData(s3Bucket, stateMachineFile)
        .then(function (data) {
            var initialParameters = data.Body.toString();
            var stateMachineInputJSON = createStateMachineInitialInput(initialParameters, event);
            console.log("State machine input JSON: " + JSON.stringify(stateMachineInputJSON));
            return stateMachineInputJSON;
        })
        .then(function (stateMachineInputJSON) {
            return triggerStateMachineExecution(stateMachineArn, stateMachineInputJSON);
        })
        .then(function (triggerStateMachineOutput) {
            var continuationToken = { "stateMachineExecutionArn": triggerStateMachineOutput.executionArn };
            var message = "State machine has been triggered: " + JSON.stringify(triggerStateMachineOutput) + ", continuationToken: " + JSON.stringify(continuationToken);
            return continueExecution(Util.jobId(event), continuationToken, callback, message);
        })
        .catch(function (err) {
            console.log("Error triggering state machine: " + stateMachineArn + ", Error: " + err.message);
            failure(Util.jobId(event), callback, context.invokeid, err.message);
        })
}

The Trigger Lambda function fetches the state machine input parameters from an S3 file, triggers the execution of the state machine using the input parameters and the stateMachineArn environment variable, and signals to CodePipeline that the execution should continue later by passing a continuation token that contains the state machine execution ARN. In case any of these operations fail and an exception is thrown, the Trigger Lambda function will fail the pipeline immediately by signaling a pipeline failure through the putJobFailureResult CodePipeline API.

If the Lambda function is continuing a previous execution, it will extract the state machine execution ARN from the continuation token and check the status of the state machine, as shown here.

function monitorStateMachineExecution(event, context, callback) {
    var stateMachineArn = process.env.stateMachineArn;
    var continuationToken = JSON.parse(Util.continuationToken(event));
    var stateMachineExecutionArn = continuationToken.stateMachineExecutionArn;
    getStateMachineExecutionStatus(stateMachineExecutionArn)
        .then(function (response) {
            if (response.status === "RUNNING") {
                var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " is still " + response.status;
                return continueExecution(Util.jobId(event), continuationToken, callback, message);
            }
            if (response.status === "SUCCEEDED") {
                var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " has: " + response.status;
                return success(Util.jobId(event), callback, message);
            }
            // FAILED, TIMED_OUT, ABORTED
            var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " has: " + response.status;
            return failure(Util.jobId(event), callback, context.invokeid, message);
        })
        .catch(function (err) {
            var message = "Error monitoring execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + ", Error: " + err.message;
            failure(Util.jobId(event), callback, context.invokeid, message);
        });
}

If the state machine is in the RUNNING state, the Lambda function will send the continuation token back to the CodePipeline action. This will cause CodePipeline to call the Lambda function again a few seconds later. If the state machine has SUCCEEDED, then the Lambda function will notify the CodePipeline action that the action has succeeded. In any other case (FAILURE, TIMED-OUT, or ABORT), the Lambda function will fail the pipeline action.

This behavior is especially useful for developers who are building and debugging a new state machine because a bug in the state machine can potentially leave the pipeline action hanging for long periods of time until it times out. The Trigger Lambda function prevents this.

Also, by having the Trigger Lambda function as a means to decouple the pipeline and state machine, we make the state machine more reusable. It can be triggered from anywhere, not just from a CodePipeline action.

The Pipeline in CodePipeline

Our sample pipeline contains two simple stages: the Source stage represented by a CodeCommit Git repository and the Prod stage, which contains the Deploy action that invokes the Trigger Lambda function. When the state machine decides that the change set created must be rejected (because it replaces or deletes some the existing production resources), it fails the pipeline without performing any updates to the existing infrastructure. (See the failed Deploy action in red.) Otherwise, the pipeline action succeeds, indicating that the existing provisioned infrastructure was either created (first run) or updated without impacting any resources. (See the green Deploy stage in the pipeline on the left.)

The Pipeline in CodePipeline

The JSON spec for the pipeline’s Prod stage is shown here. We use the UserParameters attribute to pass the S3 bucket and state machine input file to the Lambda function. These parameters are action-specific, which means that we can reuse the state machine in another pipeline action.

{
  "name": "Prod",
  "actions": [
      {
          "inputArtifacts": [
              {
                  "name": "CodeCommitOutput"
              }
          ],
          "name": "Deploy",
          "actionTypeId": {
              "category": "Invoke",
              "owner": "AWS",
              "version": "1",
              "provider": "Lambda"
          },
          "outputArtifacts": [],
          "configuration": {
              "FunctionName": "StateMachineTriggerLambda",
              "UserParameters": "{\"s3Bucket\": \"codepipeline-StepFunctions-sample\", \"stateMachineFile\": \"state_machine_input.json\"}"
          },
          "runOrder": 1
      }
  ]
}

Conclusion

In this blog post, we discussed how state machines in AWS Step Functions can be used to handle workflow-driven actions. We showed how a Lambda function can be used to fully decouple the pipeline and the state machine and manage their interaction. The use of a state machine greatly simplified the associated CodePipeline action, allowing us to build a much simpler and cleaner pipeline while drilling down into the state machine’s execution for troubleshooting or debugging.

Here are two exercises you can complete by using the source code.

Exercise #1: Do not fail the state machine and pipeline action after inspecting a change set that deletes or replaces resources. Instead, create a stack with a different name (think of blue/green deployments). You can do this by creating a state machine transition between the “Safe to Update Infra?” and “Create Stack” stages and passing a new stack name as input to the “Create Stack” stage.

Exercise #2: Add wait logic to the state machine to wait until the change set completes its execution before allowing the state machine to proceed to the “Deployment Succeeded” stage. Use the stack creation case as an example. You’ll have to create a Lambda function (similar to the Lambda function that checks the creation status of a stack) to get the creation status of the change set.

Have fun and share your thoughts!

About the Author

Marcilio Mendonca is a Sr. Consultant in the Canadian Professional Services Team at Amazon Web Services. He has helped AWS customers design, build, and deploy best-in-class, cloud-native AWS applications using VMs, containers, and serverless architectures. Before he joined AWS, Marcilio was a Software Development Engineer at Amazon. Marcilio also holds a Ph.D. in Computer Science. In his spare time, he enjoys playing drums, riding his motorcycle in the Toronto GTA area, and spending quality time with his family.

Roku Shows FBI Warning to Pirate Channel Users

Post Syndicated from Ernesto original https://torrentfreak.com/roku-shows-fbi-warning-to-pirate-channel-users-171009/

In recent years it has become much easier to stream movies and TV-shows over the Internet.

Legal services such as Netflix and HBO are flourishing, but at the same time millions of people are streaming from unauthorized sources, often paired with perfectly legal streaming platforms and devices.

Hollywood insiders have dubbed this trend “Piracy 3.0” and are actively working with stakeholders to address the threat. One of the companies rightsholders are working with is Roku, known for its easy-to-use media players.

Earlier this year a Mexican court ordered retailers to take the Roku media player off the shelves. This legal battle is still ongoing, but it was a clear signal to the company, which now has its own anti-piracy team.

Several third-party “private” channels have been removed from the player in recent weeks as they violate Roku’s terms and conditions. These include the hugely popular streaming channel XTV, which offered access to infringing content.

After its removal, XTV briefly returned as XTV 2, but that didn’t last for long. The infringing channel was soon removed again, this time showing the FBI’s anti-piracy seal followed by a rather ominous message.

“FBI Anti-Piracy Warning: Unauthorized copying is punishable under federal law,” it reads. “Roku has removed this unauthorized service due to repeated claims of copyright infringement.”

FBI Warning (via Cordcuttersnews)

The unusual warning was picked up by Cordcuttersnews and states that Roku itself removed the channel.

To some it may seem that the FBI is cracking down on Roku channels, but this is not the case. The anti-piracy seal and associated warning are often used in cases where the organization is not actively involved, to add extra weight. The FBI supports this, as long as certain standards are met.

A Roku spokesperson confirmed to TorrentFreak that they’re using it on their own accord here.

“We want to send a clear message to Roku customers and to publishers that any publication of pirated content on our platform is a violation of law and our platform rules,” the company says.

“We have recently expanded the messaging that we display to customers that install non-certified channels to alert them to the associated risks, and we display the FBI’s publicly available warning when we remove channels for copyright violations.”

The strong language shows that Roku is taking its efforts to crack down on infringing channels very seriously. A few weeks ago the company started to warn users that pirate channels may be removed without prior notice.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Microcell through a mobile hotspot

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/10/microcell-through-mobile-hotspot.html

I accidentally acquired a tree farm 20 minutes outside of town. For utilities, it gets electricity and basic phone. It doesn’t get water, sewer, cable, or DSL (i.e. no Internet). Also, it doesn’t really get cell phone service. While you can get SMS messages up there, you usually can’t get a call connected, or hold a conversation if it does.

We have found a solution — an evil solution. We connect an AT&T “Microcell“, which provides home cell phone service through your Internet connection, to an AT&T Mobile Hotspot, which provides an Internet connection through your cell phone service.

Now, you may be laughing at this, because it’s a circular connection. It’s like trying to make a sailboat go by blowing on the sails, or lifting up a barrel to lighten the load in the boat.

But it actually works.

Since we get some, but not enough, cellular signal, we setup a mast 20 feet high with a directional antenna pointed to the cell tower 7.5 miles to the southwest, connected to a signal amplifier. It’s still an imperfect solution, as we are still getting terrain distortions in the signal, but it provides a good enough signal-to-noise ratio to get a solid connection.

We then connect that directional antenna directly to a high-end Mobile Hotspot. This gives us a solid 2mbps connection with a latency under 30milliseconds. This is far lower than the 50mbps you can get right next to a 4G/LTE tower, but it’s still pretty good for our purposes.

We then connect the AT&T Microcell to the Mobile Hotspot, via WiFi.

To avoid the circular connection, we lock the frequencies for the Mobile Hotspot to 4G/LTE, and to 3G for the Microcell. This prevents the Mobile Hotspot locking onto the strong 3G signal from the Microcell. It also prevents the two from causing noise to the other.

This works really great. We now get a strong cell signal on our phones even 400 feet from the house through some trees. We can be all over the property, out in the lake, down by the garden, and so on, and have our phones work as normal. It’s only AT&T, but that’s what the whole family uses.

You might be asking why we didn’t just use a normal signal amplifier, like they use on corporate campus. It boosts all the analog frequencies, making any cell phone service works.

We’ve tried this, and it works a bit, allowing cell phones to work inside the house pretty well. But they don’t work outside the house, which is where we spend a lot of time. In addition, while our newer phones work, my sister’s iPhone 5 doesn’t. We have no idea what’s going on. Presumably, we could hire professional installers and stuff to get everything working, but nobody would quote us a price lower than $25,000 to even come look at the property.

Another possible solution is satellite Internet. There are two satellites in orbit that cover the United States with small “spot beams” delivering high-speed service (25mbps downloads). However, the latency is 500milliseconds, which makes it impractical for low-latency applications like phone calls.

While I know a lot about the technology in theory, I find myself hopelessly clueless in practice. I’ve been playing with SDR (“software defined radio”) to try to figure out exactly where to locate and point the directional antenna, but I’m not sure I’ve come up with anything useful. In casual tests, it seems rotating the antenna from vertical to horizontal increases the signal-to-noise ratio a bit, which seems counter intuitive, and should not happen. So I’m completely lost.

Anyway, I thought I’d write this up as a blogpost, in case anybody has better suggestion. Or, instead of signals, suggestions to get wired connectivity. Properties a half mile away get DSL, I wish I knew who to talk to at the local phone company to pay them money to extend Internet to our property.

Phone works in all this area now

GPS Spoofing Attacks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/09/gps_spoofing_at.html

Wired has a story about a possible GPS spoofing attack by Russia:

After trawling through AIS data from recent years, evidence of spoofing becomes clear. Goward says GPS data has placed ships at three different airports and there have been other interesting anomalies. “We would find very large oil tankers who could travel at the maximum speed at 15 knots,” says Goward, who was formerly director for Marine Transportation Systems at the US Coast Guard. “Their AIS, which is powered by GPS, would be saying they had sped up to 60 to 65 knots for an hour and then suddenly stopped. They had done that several times.”

All of the evidence from the Black Sea points towards a co-ordinated attempt to disrupt GPS. A recently published report from NRK found that 24 vessels appeared at Gelendzhik airport around the same time as the Atria. When contacted, a US Coast Guard representative refused to comment on the incident, saying any GPS disruption that warranted further investigation would be passed onto the Department of Defence.

“It looks like a sophisticated attack, by somebody who knew what they were doing and were just testing the system,” Bonenberg says. Humphreys told NRK it “strongly” looks like a spoofing incident. Fire Eye’s Brubaker, agreed, saying the activity looked intentional. Goward is also confident that GPS were purposely disrupted. “What this case shows us is there are entities out there that are willing and eager to disrupt satellite navigation systems for whatever reason and they can do it over a fairly large area and in a sophisticated way,” he says. “They’re not just broadcasting a stronger signal and denying service this is worse they’re providing hazardously misleading information.”

Boston Red Sox Caught Using Technology to Steal Signs

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/09/boston_red_sox_.html

The Boston Red Sox admitted to eavesdropping on the communications channel between catcher and pitcher.

Stealing signs is believed to be particularly effective when there is a runner on second base who can both watch what hand signals the catcher is using to communicate with the pitcher and can easily relay to the batter any clues about what type of pitch may be coming. Such tactics are allowed as long as teams do not use any methods beyond their eyes. Binoculars and electronic devices are both prohibited.

In recent years, as cameras have proliferated in major league ballparks, teams have begun using the abundance of video to help them discern opponents’ signs, including the catcher’s signals to the pitcher. Some clubs have had clubhouse attendants quickly relay information to the dugout from the personnel monitoring video feeds.

But such information has to be rushed to the dugout on foot so it can be relayed to players on the field — a runner on second, the batter at the plate — while the information is still relevant. The Red Sox admitted to league investigators that they were able to significantly shorten this communications chain by using electronics. In what mimicked the rhythm of a double play, the information would rapidly go from video personnel to a trainer to the players.

This is ridiculous. The rules about what sorts of sign stealing are allowed and what sorts are not are arbitrary and unenforceable. My guess is that the only reason there aren’t more complaints is because everyone does it.

The Red Sox responded in kind on Tuesday, filing a complaint against the Yankees claiming that the team uses a camera from its YES television network exclusively to steal signs during games, an assertion the Yankees denied.

Boston’s mistake here was using a very conspicuous Apple Watch as a communications device. They need to learn to be more subtle, like everyone else.

timeShift(GrafanaBuzz, 1w) Issue 14

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/09/22/timeshiftgrafanabuzz-1w-issue-14/

Summer is officially in the rear-view mirror, but we at Grafana Labs are excited. Next week, the team will gather in Stockholm, Sweden where we’ll be discussing Grafana 5.0, GrafanaCon EU and setting other goals. If you’re attending Percona Live Europe 2017 in Dublin, be sure and catch Grafana developer, Daniel Lee on Tuesday, September 26. He’ll be showing off the new MySQL data source and a sneak peek of Grafana 5.0.

And with that – we hope you enjoy this issue of TimeShift!


Latest Release

Grafana 4.5.2 is now available! Various fixes to the Graphite data source, HTTP API, and templating.

To see details on what’s been fixed in the newest version, please see the release notes.

Download Grafana 4.5.2 Now


From the Blogosphere

A Monitoring Solution for Docker Hosts, Containers and Containerized Services: Stefan was searching for an open source, self-hosted monitoring solution. With an ever-growing number of open source TSDBs, Stefan outlines why he chose Prometheus and provides a rundown of how he’s monitoring his Docker hosts, containers and services.

Real-time API Performance Monitoring with ES, Beats, Logstash and Grafana: As APIs become a centerpiece for businesses, monitoring API performance is extremely important. Hiren recently configured real time API response time monitoring for a project and shares his implementation plan and configurations.

Monitoring SSL Certificate Expiry in GCP and Kubernetes: This article discusses how to use Prometheus and Grafana to automatically monitor SSL certificates in use by load balancers across GCP projects.

Node.js Performance Monitoring with Prometheus: This is a good primer for monitoring in general. It discusses what monitoring is, important signals to know, instrumentation, and things to consider when selecting a monitoring tool.

DIY Dashboard with Grafana and MariaDB: Mark was interested in testing out the new beta MySQL support in Grafana, so he wrote a short article on how he is using Grafana with MariaDB.

Collecting Temperature Data with Raspberry Pi Computers: Many of us use monitoring for tracking mission-critical systems, but setting up environment monitoring can be a fun way to improve your programming skills as well.


GrafanaCon EU CFP is Open

Have a big idea to share? A shorter talk or a demo you’d like to show off? We’re looking for technical and non-technical talks of all sizes. The proposals are rolling in, but we are happy to save a speaking slot for you!

I’d Like to Speak at GrafanaCon


Grafana Plugins

There were a lot of plugin updates to highlight this week, many of which were due to changes in Grafana 4.5. It’s important to keep your plugins up to date, since bug fixes and new features are added frequently. We’ve made the process of installing and updating plugins simple. On an on-prem instance, use the Grafana-cli, or on Hosted Grafana, install and update with 1-click.

NEW PLUGIN

Linksmart HDS Data Source – The LinkSmart Historical Data Store is a new Grafana data source plugin. LinkSmart is an open source IoT platform for developing IoT applications. IoT applications need to deal with large amounts of data produced by a growing number of sensors and other devices. The Historical Datastore is for storing, querying, and aggregating (time-series) sensor data.

Install Now

UPDATED PLUGIN

Simple JSON Data Source – This plugin received a bug fix for the query editor.

Update Now

UPDATED PLUGIN

Stagemonitor Elasticsearch App – Numerous small updates and the version updated to match the StageMonitor version number.

Update Now

UPDATED PLUGIN

Discrete Panel – Update to fix breaking change in Grafana 4.5.

Update Now

UPDATED PLUGIN

Status Dot Panel – Minor HTML Update in this version.

Update Now

UPDATED PLUGIN

Alarm Box Panel – This panel was updated to fix breaking changes in Grafana 4.5.

Update Now


This week’s MVC (Most Valuable Contributor)

Each week we highlight a contributor to Grafana or the surrounding ecosystem as a thank you for their participation in making open source software great.

Sven Klemm opened a PR for adding a new Postgres data source and has been very quick at implementing proposed changes. The Postgres data source is on our roadmap for Grafana 5.0 so this PR really helps. Thanks Sven!


Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

Glad you’re finding Grafana useful! Curious about that annotation just before midnight 🙂

We Need Your Help

Last week we announced an experiment we were conducting, and need your help! Do you have a graph that you love because the data is beautiful or because the graph provides interesting information? Please get in touch. Tweet or send us an email with a screenshot, and we’ll tell you about this fun experiment.

I Want to Help


Grafana Labs is Hiring!

We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

Check out our Open Positions


What do you think?

What would you like to see here? Submit a comment on this article below, or post something at our community forum. Help us make these weekly roundups better!

Follow us on Twitter, like us on Facebook, and join the Grafana Labs community.

AWS Hot Startups – August 2017

Post Syndicated from Tina Barr original https://aws.amazon.com/blogs/aws/aws-hot-startups-august-2017/

There’s no doubt about it – Artificial Intelligence is changing the world and how it operates. Across industries, organizations from startups to Fortune 500s are embracing AI to develop new products, services, and opportunities that are more efficient and accessible for their consumers. From driverless cars to better preventative healthcare to smart home devices, AI is driving innovation at a fast rate and will continue to play a more important role in our everyday lives.

This month we’d like to highlight startups using AI solutions to help companies grow. We are pleased to feature:

  • SignalBox – a simple and accessible deep learning platform to help businesses get started with AI.
  • Valossa – an AI video recognition platform for the media and entertainment industry.
  • Kaliber – innovative applications for businesses using facial recognition, deep learning, and big data.

SignalBox (UK)

In 2016, SignalBox founder Alain Richardt was hearing the same comments being made by developers, data scientists, and business leaders. They wanted to get into deep learning but didn’t know where to start. Alain saw an opportunity to commodify and apply deep learning by providing a platform that does the heavy lifting with an easy-to-use web interface, blueprints for common tasks, and just a single-click to productize the models. With SignalBox, companies can start building deep learning models with no coding at all – they just select a data set, choose a network architecture, and go. SignalBox also offers step-by-step tutorials, tips and tricks from industry experts, and consulting services for customers that want an end-to-end AI solution.

SignalBox offers a variety of solutions that are being used across many industries for energy modeling, fraud detection, customer segmentation, insurance risk modeling, inventory prediction, real estate prediction, and more. Existing data science teams are using SignalBox to accelerate their innovation cycle. One innovative UK startup, Energi Mine, recently worked with SignalBox to develop deep networks that predict anomalous energy consumption patterns and do time series predictions on energy usage for businesses with hundreds of sites.

SignalBox uses a variety of AWS services including Amazon EC2, Amazon VPC, Amazon Elastic Block Store, and Amazon S3. The ability to rapidly provision EC2 GPU instances has been a critical factor in their success – both in terms of keeping their operational expenses low, as well as speed to market. The Amazon API Gateway has allowed for operational automation, giving SignalBox the ability to control its infrastructure.

To learn more about SignalBox, visit here.

Valossa (Finland)

As students at the University of Oulu in Finland, the Valossa founders spent years doing research in the computer science and AI labs. During that time, the team witnessed how the world was moving beyond text, with video playing a greater role in day-to-day communication. This spawned an idea to use technology to automatically understand what an audience is viewing and share that information with a global network of content producers. Since 2015, Valossa has been building next generation AI applications to benefit the media and entertainment industry and is moving beyond the capabilities of traditional visual recognition systems.

Valossa’s AI is capable of analyzing any video stream. The AI studies a vast array of data within videos and converts that information into descriptive tags, categories, and overviews automatically. Basically, it sees, hears, and understands videos like a human does. The Valossa AI can detect people, visual and auditory concepts, key speech elements, and labels explicit content to make moderating and filtering content simpler. Valossa’s solutions are designed to provide value for the content production workflow, from media asset management to end-user applications for content discovery. AI-annotated content allows online viewers to jump directly to their favorite scenes or search specific topics and actors within a video.

Valossa leverages AWS to deliver the industry’s first complete AI video recognition platform. Using Amazon EC2 GPU instances, Valossa can easily scale their computation capacity based on customer activity. High-volume video processing with GPU instances provides the necessary speed for time-sensitive workflows. The geo-located Availability Zones in EC2 allow Valossa to bring resources close to their customers to minimize network delays. Valossa also uses Amazon S3 for video ingestion and to provide end-user video analytics, which makes managing and accessing media data easy and highly scalable.

To see how Valossa works, check out www.WhatIsMyMovie.com or enable the Alexa Skill, Valossa Movie Finder. To try the Valossa AI, sign up for free at www.valossa.com.

Kaliber (San Francisco, CA)

Serial entrepreneurs Ray Rahman and Risto Haukioja founded Kaliber in 2016. The pair had previously worked in startups building smart cities and online privacy tools, and teamed up to bring AI to the workplace and change the hospitality industry. Our world is designed to appeal to our senses – stores and warehouses have clearly marked aisles, products are colorfully packaged, and we use these designs to differentiate one thing from another. We tell each other apart by our faces, and previously that was something only humans could measure or act upon. Kaliber is using facial recognition, deep learning, and big data to create solutions for business use. Markets and companies that aren’t typically associated with cutting-edge technology will be able to use their existing camera infrastructure in a whole new way, making them more efficient and better able to serve their customers.

Computer video processing is rapidly expanding, and Kaliber believes that video recognition will extend to far more than security cameras and robots. Using the clients’ network of in-house cameras, Kaliber’s platform extracts key data points and maps them to actionable insights using their machine learning (ML) algorithm. Dashboards connect users to the client’s BI tools via the Kaliber enterprise APIs, and managers can view these analytics to improve their real-world processes, taking immediate corrective action with real-time alerts. Kaliber’s Real Metrics are aimed at combining the power of image recognition with ML to ultimately provide a more meaningful experience for all.

Kaliber uses many AWS services, including Amazon Rekognition, Amazon Kinesis, AWS Lambda, Amazon EC2 GPU instances, and Amazon S3. These services have been instrumental in helping Kaliber meet the needs of enterprise customers in record time.

Learn more about Kaliber here.

Thanks for reading and we’ll see you next month!

-Tina

 

Journalists Generally Do Not Use Secure Communication

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

This should come as no surprise:

Alas, our findings suggest that secure communications haven’t yet attracted mass adoption among journalists. We looked at 2,515 Washington journalists with permanent credentials to cover Congress, and we found only 2.5 percent of them solicit end-to-end encrypted communication via their Twitter bios. That’s just 62 out of all the broadcast, newspaper, wire service, and digital reporters. Just 28 list a way to reach them via Signal or another secure messaging app. Only 22 provide a PGP public key, a method that allows sources to send encrypted messages. A paltry seven advertise a secure email address. In an era when anything that can be hacked will be and when the president has declared outright war on the media, this should serve as a frightening wake-up call.

[…]

When journalists don’t step up, sources with sensitive information face the burden of using riskier modes of communication to initiate contact­ — and possibly conduct all of their exchanges­ — with reporters. It increases their chances of getting caught, putting them in danger of losing their job or facing prosecution. It’s burden enough to make them think twice about whistleblowing.

I forgive them for not using secure e-mail. It’s hard to use and confusing. But secure messaging is easy.

Affordable Raspberry Pi 3D Body Scanner

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/affordable-raspberry-pi-3d-body-scanner/

With a £1000 grant from Santander, Poppy Mosbacher set out to build a full-body 3D body scanner with the intention of creating an affordable setup for makespaces and similar community groups.

First Scan from DIY Raspberry Pi Scanner

Head and Shoulders Scan with 29 Raspberry Pi Cameras

Uses for full-body 3D scanning

Poppy herself wanted to use the scanner in her work as a fashion designer. With the help of 3D scans of her models, she would be able to create custom cardboard dressmakers dummy to ensure her designs fit perfectly. This is a brilliant way of incorporating digital tech into another industry – and it’s not the only application for this sort of build. Growing numbers of businesses use 3D body scanning, for example the stores around the world where customers can 3D scan and print themselves as action-figure-sized replicas.

Print your own family right on the high street!
image c/o Tom’s Guide and Shapify

We’ve also seen the same technology used in video games for more immersive virtual reality. Moreover, there are various uses for it in healthcare and fitness, such as monitoring the effect of exercise regimes or physiotherapy on body shape or posture.

Within a makespace environment, a 3D body scanner opens the door to including new groups of people in community make projects: imagine 3D printing miniatures of a theatrical cast to allow more realistic blocking of stage productions and better set design, or annually sending grandparents a print of their grandchild so they can compare the child’s year-on-year growth in a hands-on way.

Raspberry Pi 3d Body Scan

The Germany-based clothing business Outfittery uses full body scanners to take the stress out of finding clothes that fits well.
image c/o Outfittery

As cheesy as it sounds, the only limit for the use of 3D scanning is your imagination…and maybe storage space for miniature prints.

Poppy’s Raspberry Pi 3D Body Scanner

For her build, Poppy acquired 27 Raspberry Pi Zeros and 27 Raspberry Pi Camera Modules. With various other components, some 3D-printed or made of cardboard, Poppy got to work. She was helped by members of Build Brighton and by her friend Arthur Guy, who also wrote the code for the scanner.

Raspberry Pi 3D Body Scanner

The Pi Zeros run Raspbian Lite, and are connected to a main server running a node application. Each is fitted into its own laser-cut cardboard case, and secured to a structure of cardboard tubing and 3D-printed connectors.

Raspberry Pi 3D Body Scanner

In the finished build, the person to be scanned stands within the centre of the structure, and the press of a button sends the signal for all Pis to take a photo. The images are sent back to the server, and processed through Autocade ReMake, a freemium software available for the PC (Poppy discovered part-way through the project that the Mac version has recently lost support).

Build your own

Obviously there’s a lot more to the process of building this full-body 3D scanner than what I’ve reported in these few paragraphs. And since it was Poppy’s goal to make a readily available and affordable scanner that anyone can recreate, she’s provided all the instructions and code for it on her Instructables page.

Projects like this, in which people use the Raspberry Pi to create affordable and interesting tech for communities, are exactly the type of thing we love to see. Always make sure to share your Pi-based projects with us on social media, so we can boost their visibility!

If you’re a member of a makespace, run a workshop in a school or club, or simply love to tinker and create, this build could be the perfect addition to your workshop. And if you recreate Poppy’s scanner, or build something similar, we’d love to see the results in the comments below.

The post Affordable Raspberry Pi 3D Body Scanner appeared first on Raspberry Pi.

Streaming Service iflix Buys Shows Based on Piracy Data

Post Syndicated from Ernesto original https://torrentfreak.com/streaming-service-iflix-buys-shows-based-on-piracy-data-170819/

When major movie and TV companies discuss piracy they often mention the massive losses incurred as a result of unauthorized downloads and streams.

However, this unofficial market also offers a valuable pool of often publicly available data on the media consumption habits of a relatively young generation.

Many believe that piracy is in part a market signal showing copyright holders what consumers want. This makes piracy statistics key business intelligence, which some companies have started to realize.

Netflix, for example, previously said that their offering is partly based on what shows do well on BitTorrent networks and other pirate sites. In addition, the streaming service also uses piracy to figure out how much they can charge in a country. They are not alone.

Other major entertainment companies also keep a close eye on piracy, using this data to their advantage. This includes the Asia-based streaming portal iFlix, which recently secured $133 million in funding and boasts to have over five million users.

Iflix co-founder Patrick Grove says that his company actively uses piracy numbers to determine what content they acquire. The data reveal what is popular locally, and help to give viewers the TV-shows and movies they’re most interested in.

“We looked at piracy data in every market,” Grove informed CNBC’s Managing Asia, which doesn’t stop at looking at a few torrent download numbers.

Representatives from the Asian company actually went out on the streets to buy pirated DVDs from street vendors. In addition, iflix also received help from local Internet providers which shared a variety of streaming data.

TorrentFreak reached out to the streaming service to get more details about their data gathering techniques. One of the main partners to measure online piracy is the German company TECXIPIO, which is known to actively monitor BitTorrent traffic.

The company also maintains a close relationship with Internet providers that offer further insight, including streaming data, to determine which titles work best in each market.

While analyzing the different sets of data, the streaming service was surprised to see the diversity in different regions as well as the ever-changing consumer demand.

“Through looking at the Top 20 pirated DVDs in every market we are live in, we were surprised to find the amount of pirated K-drama content. In Ghana for example, the number one pirated title is K-drama series called ‘Legend of the Blue Sea’,” an iflix spokesperson told us.

Iflix believes that piracy data is superior to other market intelligence. Before rolling out its service in Saudi Arabia the company made a list of the 1,000 most popular shows and used that to its advantage.

While there is a lot of piracy in emerging markets, iflix doesn’t think that people are not willing to pay for entertainment. It just has to be available for a decent price, and that’s where they come in.

“We believe that people in emerging markets do not actively want to steal content, they do so because there is no better alternative,” the company informs us.

“As consumers become more connected, gaining access to information and cultural influences on a global scale, they want to be entertained at a world-class standard. We set out with the aim of offering an alternative that is better than piracy; by providing unlimited access to high-quality, world-class entertainment, all at the price of pirated DVD.”

There is no doubt that iflix is ambitious, and that it’s willing to employ some unusual tactics to grow its userbase. The company is quite optimistic about the future as well, judging from its co-founder’s prediction that it will welcome its billionth viewer in a few years.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Updates to GPIO Zero, the physical computing API

Post Syndicated from Ben Nuttall original https://www.raspberrypi.org/blog/gpio-zero-update/

GPIO Zero v1.4 is out now! It comes with a set of new features, including a handy pinout command line tool. To start using this newest version of the API, update your Raspbian OS now:

sudo apt update && sudo apt upgrade

Some of the things we’ve added will make it easier for you try your hand on different programming styles. In doing so you’ll build your coding skills, and will improve as a programmer. As a consequence, you’ll learn to write more complex code, which will enable you to take on advanced electronics builds. And on top of that, you can use the skills you’ll acquire in other computing projects.

GPIO Zero pinout tool

The new pinout tool

Developing GPIO Zero

Nearly two years ago, I started the GPIO Zero project as a simple wrapper around the low-level RPi.GPIO library. I wanted to create a simpler way to control GPIO-connected devices in Python, based on three years’ experience of training teachers, running workshops, and building projects. The idea grew over time, and the more we built for our Python library, the more sophisticated and powerful it became.

One of the great things about Python is that it’s a multi-paradigm programming language. You can write code in a number of different styles, according to your needs. You don’t have to write classes, but you can if you need them. There are functional programming tools available, but beginners get by without them. Importantly, the more advanced features of the language are not a barrier to entry.

Become a more advanced programmer

As a beginner to programming, you usually start by writing procedural programs, in which the flow moves from top to bottom. Then you’ll probably add loops and create your own functions. Your next step might be to start using libraries which introduce new patterns that operate in a different manner to what you’ve written before, for example threaded callbacks (event-driven programming). You might move on to object-oriented programming, extending the functionality of classes provided by other libraries, and starting to write your own classes. Occasionally, you may make use of tools created with functional programming techniques.

Five buttons in different colours

Take control of the buttons in your life

It’s much the same with GPIO Zero: you can start using it very easily, and we’ve made it simple to progress along the learning curve towards more advanced programming techniques. For example, if you want to make a push button control an LED, the easiest way to do this is via procedural programming using a while loop:

from gpiozero import LED, Button

led = LED(17)
button = Button(2)

while True:
    if button.is_pressed:
        led.on()
    else:
        led.off()

But another way to achieve the same thing is to use events:

from gpiozero import LED, Button
from signal import pause

led = LED(17)
button = Button(2)

button.when_pressed = led.on
button.when_released = led.off

pause()

You could even use a declarative approach, and set the LED’s behaviour in a single line:

from gpiozero import LED, Button
from signal import pause

led = LED(17)
button = Button(2)

led.source = button.values

pause()

You will find that using the procedural approach is a great start, but at some point you’ll hit a limit, and will have to try a different approach. The example above can be approach in several programming styles. However, if you’d like to control a wider range of devices or a more complex system, you need to carefully consider which style works best for what you want to achieve. Being able to choose the right programming style for a task is a skill in itself.

Source/values properties

So how does the led.source = button.values thing actually work?

Every GPIO Zero device has a .value property. For example, you can read a button’s state (True or False), and read or set an LED’s state (so led.value = True is the same as led.on()). Since LEDs and buttons operate with the same value set (True and False), you could say led.value = button.value. However, this only sets the LED to match the button once. If you wanted it to always match the button’s state, you’d have to use a while loop. To make things easier, we came up with a way of telling devices they’re connected: we added a .values property to all devices, and a .source to output devices. Now, a loop is no longer necessary, because this will do the job:

led.source = button.values

This is a simple approach to connecting devices using a declarative style of programming. In one single line, we declare that the LED should get its values from the button, i.e. when the button is pressed, the LED should be on. You can even mix the procedural with the declarative style: at one stage of the program, the LED could be set to match the button, while in the next stage it could just be blinking, and finally it might return back to its original state.

These additions are useful for connecting other devices as well. For example, a PWMLED (LED with variable brightness) has a value between 0 and 1, and so does a potentiometer connected via an ADC (analogue-digital converter) such as the MCP3008. The new GPIO Zero update allows you to say led.source = pot.values, and then twist the potentiometer to control the brightness of the LED.

But what if you want to do something more complex, like connect two devices with different value sets or combine multiple inputs?

We provide a set of device source tools, which allow you to process values as they flow from one device to another. They also let you send in artificial values such as random data, and you can even write your own functions to generate values to pass to a device’s source. For example, to control a motor’s speed with a potentiometer, you could use this code:

from gpiozero import Motor, MCP3008
from signal import pause

motor = Motor(20, 21)
pot = MCP3008()

motor.source = pot.values

pause()

This works, but it will only drive the motor forwards. If you wanted the potentiometer to drive it forwards and backwards, you’d use the scaled tool to scale its values to a range of -1 to 1:

from gpiozero import Motor, MCP3008
from gpiozero.tools import scaled
from signal import pause

motor = Motor(20, 21)
pot = MCP3008()

motor.source = scaled(pot.values, -1, 1)

pause()

And to separately control a robot’s left and right motor speeds with two potentiometers, you could do this:

from gpiozero import Robot, MCP3008
from signal import pause

robot = Robot(left=(2, 3), right=(4, 5))
left = MCP3008(0)
right = MCP3008(1)

robot.source = zip(left.values, right.values)

pause()

GPIO Zero and Blue Dot

Martin O’Hanlon created a Python library called Blue Dot which allows you to use your Android device to remotely control things on their Raspberry Pi. The API is very similar to GPIO Zero, and it even incorporates the value/values properties, which means you can hook it up to GPIO devices easily:

from bluedot import BlueDot
from gpiozero import LED
from signal import pause

bd = BlueDot()
led = LED(17)

led.source = bd.values

pause()

We even included a couple of Blue Dot examples in our recipes.

Make a series of binary logic gates using source/values

Read more in this source/values tutorial from The MagPi, and on the source/values documentation page.

Remote GPIO control

GPIO Zero supports multiple low-level GPIO libraries. We use RPi.GPIO by default, but you can choose to use RPIO or pigpio instead. The pigpio library supports remote connections, so you can run GPIO Zero on one Raspberry Pi to control the GPIO pins of another, or run code on a PC (running Windows, Mac, or Linux) to remotely control the pins of a Pi on the same network. You can even control two or more Pis at once!

If you’re using Raspbian on a Raspberry Pi (or a PC running our x86 Raspbian OS), you have everything you need to remotely control GPIO. If you’re on a PC running Windows, Mac, or Linux, you just need to install gpiozero and pigpio using pip. See our guide on configuring remote GPIO.

I road-tested the new pin_factory syntax at the Raspberry Jam @ Pi Towers

There are a number of different ways to use remote pins:

  • Set the default pin factory and remote IP address with environment variables:
$ GPIOZERO_PIN_FACTORY=pigpio PIGPIO_ADDR=192.168.1.2 python3 blink.py
  • Set the default pin factory in your script:
import gpiozero
from gpiozero import LED
from gpiozero.pins.pigpio import PiGPIOFactory

gpiozero.Device.pin_factory = PiGPIOFactory(host='192.168.1.2')

led = LED(17)
  • The pin_factory keyword argument allows you to use multiple Pis in the same script:
from gpiozero import LED
from gpiozero.pins.pigpio import PiGPIOFactory

factory2 = PiGPIOFactory(host='192.168.1.2')
factory3 = PiGPIOFactory(host='192.168.1.3')

local_hat = TrafficHat()
remote_hat2 = TrafficHat(pin_factory=factory2)
remote_hat3 = TrafficHat(pin_factory=factory3)

This is a really powerful feature! For more, read this remote GPIO tutorial in The MagPi, and check out the remote GPIO recipes in our documentation.

GPIO Zero on your PC

GPIO Zero doesn’t have any dependencies, so you can install it on your PC using pip. In addition to the API’s remote GPIO control, you can use its ‘mock’ pin factory on your PC. We originally created the mock pin feature for the GPIO Zero test suite, but we found that it’s really useful to be able to test GPIO Zero code works without running it on real hardware:

$ GPIOZERO_PIN_FACTORY=mock python3
>>> from gpiozero import LED
>>> led = LED(22)
>>> led.blink()
>>> led.value
True
>>> led.value
False

You can even tell pins to change state (e.g. to simulate a button being pressed) by accessing an object’s pin property:

>>> from gpiozero import LED
>>> led = LED(22)
>>> button = Button(23)
>>> led.source = button.values
>>> led.value
False
>>> button.pin.drive_low()
>>> led.value
True

You can also use the pinout command line tool if you set your pin factory to ‘mock’. It gives you a Pi 3 diagram by default, but you can supply a revision code to see information about other Pi models. For example, to use the pinout tool for the original 256MB Model B, just type pinout -r 2.

GPIO Zero documentation and resources

On the API’s website, we provide beginner recipes and advanced recipes, and we have added remote GPIO configuration including PC/Mac/Linux and Pi Zero OTG, and a section of GPIO recipes. There are also new sections on source/values, command-line tools, FAQs, Pi information and library development.

You’ll find plenty of cool projects using GPIO Zero in our learning resources. For example, you could check out the one that introduces physical computing with Python and get stuck in! We even provide a GPIO Zero cheat sheet you can download and print.

There are great GPIO Zero tutorials and projects in The MagPi magazine every month. Moreover, they also publish Simple Electronics with GPIO Zero, a book which collects a series of tutorials useful for building your knowledge of physical computing. And the best thing is, you can download it, and all magazine issues, for free!

Check out the API documentation and read more about what’s new in GPIO Zero on my blog. We have lots planned for the next release. Watch this space.

Get building!

The world of physical computing is at your fingertips! Are you feeling inspired?

If you’ve never tried your hand on physical computing, our Build a robot buggy learning resource is the perfect place to start! It’s your step-by-step guide for building a simple robot controlled with the help of GPIO Zero.

If you have a gee-whizz idea for an electronics project, do share it with us below. And if you’re currently working on a cool build and would like to show us how it’s going, pop a link to it in the comments.

The post Updates to GPIO Zero, the physical computing API appeared first on Raspberry Pi.

New – Amazon Connect and Amazon Lex Integration

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/new-amazon-connect-and-amazon-lex-integration/

I’m really excited to share some recent enhancements to two of my favorite services: Amazon Connect and Amazon Lex. Amazon Connect is a self-service, cloud-based contact center service that makes it easy for any business to deliver better customer service at lower cost. Amazon Lex is a service for building conversational interfaces using voice and text. By integrating these two services you can take advantage of Lex‘s automatic speech recognition (ASR) and natural language processing/understading (NLU) capabilities to create great self-service experiences for your customers. To enable this integration the Lex team added support for 8kHz speech input – more on that later. Why should you care about this? Well, if the a bot can solve the majority of your customer’s requests your customers spend less time waiting on hold and more time using your products.

If you need some more background on Amazon Connect or Lex I strongly recommend Jeff’s previous posts[1][2] on these services – especially if you like LEGOs.


Let’s dive in and learn to use this new integration. We’ll take an application that we built on our Twitch channel and modify it for this blog. At the application’s core a user calls an Amazon Connect number which connects them to an Lex bot which invokes an AWS Lambda function based on an intent from Lex. So what does our little application do?

I want to finally settle the question of what the best code editor is: I like vim, it’s a spectacular editor that does one job exceptionally well – editing code (it’s the best). My colleague Jeff likes emacs, a great operating system editor… if you were born with extra joints in your fingers. My colleague Tara loves Visual Studio and sublime. Rather than fighting over what the best editor is I thought we might let you, dear reader, vote. Don’t worry you can even vote for butterflies.

Interested in voting? Call +1 614-569-4019 and tell us which editor you’re voting for! We don’t store your number or record your voice so feel free to vote more than once for vim. Want to see the votes live? http://best-editor-ever.s3-website-us-east-1.amazonaws.com/.

Now, how do we build this little contraption? We’ll cover each component but since we’ve talked about Lex and Lambda before we’ll focus mostly on the Amazon Connect component. I’m going to assume you already have a connect instance running.

Amazon Lex

Let’s start with the Lex side of things. We’ll create a bot named VoteEditor with two intents: VoteEditor with a single slot called editor and ConnectToAgent with no slots. We’ll populate our editor slot full of different code editor names (maybe we’ll leave out emacs).

AWS Lambda

Our Lambda function will also be fairly simple. First we’ll create a Amazon DynamoDB table to store our votes. Then we’ll make a helper method to respond to Lex (build_response) – it will just wrap our message in a Lex friendly response format. Now we just have to figure out our flow logic.


def lambda_handler(event, context):
    if 'ConnectToAgent' == event['currentIntent']['name']:
        return build_response("Ok, connecting you to an agent.")
    elif 'VoteEditor' == event['currentIntent']['name']:
        editor = event['currentIntent']['slots']['editor']
        resp = ddb.update_item(
            Key={"name": editor.lower()},
            UpdateExpression="SET votes = :incr + if_not_exists(votes, :default)",
            ExpressionAttributeValues={":incr": 1, ":default": 0},
            ReturnValues="ALL_NEW"
        )
        msg = "Awesome, now {} has {} votes!".format(
            resp['Attributes']['name'],
            resp['Attributes']['votes'])
        return build_response(msg)

Let’s make sure we understand the code. So, if we got a vote for an editor and it doesn’t exist yet then we add that editor with 1 vote. Otherwise we increase the number of votes on that editor by 1. If we get a request for an agent, we terminate the flow with a nice message. Easy. Now we just tell our Lex bot to use our Lambda function to fulfill our intents. We can test that everything is working over text in the Lex console before moving on.

Amazon Connect

Before we can use our Lex bot in a Contact Flow we have to make sure our Amazon Connect instance has access to it. We can do this by hopping over to the Amazon Connect service console, selecting our instance, and navigating to “Contact Flows”. There should be a section called Lex where you can add your bots!

Now that our Amazon Connect instance can invoke our Lex bot we can create a new Contact Flow that contains our Lex bot. We add the bot to our flow through the “Get customer input” widget from the “Interact” category.

Once we’re on the widget we have a “DTMF” tab for taking input from number keys on a phone or the “Amazon Lex” tab for taking voiceinput and passing it to the Lex service. We’ll use the Lex tab and put in some configuration.

Lots of options, but in short we add the bot we want to use (including the version of the bot), the intents we want to use from our bot, and a short prompt to introduce the bot (and mayb prompt the customer for input).

Our final contact flow looks like this:

A real world example might allow a customer to perform many transactions through a Lex bot. Then on an error or ConnectToAgent intent put the customer into a queue where they could talk to a real person. It could collect and store information about users and populate a rich interface for an agent to use so they could jump right into the conversation with all the context they need.

I want to especially highlight the advantage of 8kHz audio support in Lex. Lex originally only supported speech input that was sampled at a higher rate than the 8 kHz input from the phone. Modern digital communication appliations typically use audio signals sampled at a minimum of 16 kHz. This higher fidelity recroding makes it easier differentiate between sounds like “ess” (/s/) and “eff” (/f/) – or so the audio experts tell me. Phones, however, use a much lower quality recording. Humans, and their ears, are pretty good at using surrounding words to figure out what a voice is saying from a lower quality recording (just check the NASA apollo recordings for proof of this). Most digital phone systems are setup to use 8 kHz sampling by default – it’s a nice tradeoff in bandwidth and fidelity. That’s why your voice sometimes sounds different on the phone. On top of this fundmental sampling rate issue you also have to deal with the fact that a lot of phone call data is already lossy (can you hear me now?). There are thousands of different devices from hundreds of different manufacturers, and tons of different software implentations. So… how do you solve this recognition issue?

The Lex team decided that the best way to address this was to expand the set of models they were using for speech recognition to include an 8kHz model. Support for an 8 kHz telephony audio sampling rate provides increased speech recognition accuracy and fidelity for your contact center interactions. This was a great effort by the team that enables a lot of customers to do more with Amazon Connect.

One final note is that Amazon Connect uses the exact same PostContent endpoint that you can use as an external developer so you don’t have to be a Amazon Connect user to take advantage of this 8kHz feature in Lex.

I hope you guys enjoyed this post and as always the real details are in the docs and API Reference.

Randall

NSA Collects MS Windows Error Information

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

Back in 2013, Der Spiegel reported that the NSA intercepts and collects Windows bug reports:

One example of the sheer creativity with which the TAO spies approach their work can be seen in a hacking method they use that exploits the error-proneness of Microsoft’s Windows. Every user of the operating system is familiar with the annoying window that occasionally pops up on screen when an internal problem is detected, an automatic message that prompts the user to report the bug to the manufacturer and to restart the program. These crash reports offer TAO specialists a welcome opportunity to spy on computers.

When TAO selects a computer somewhere in the world as a target and enters its unique identifiers (an IP address, for example) into the corresponding database, intelligence agents are then automatically notified any time the operating system of that computer crashes and its user receives the prompt to report the problem to Microsoft. An internal presentation suggests it is NSA’s powerful XKeyscore spying tool that is used to fish these crash reports out of the massive sea of Internet traffic.

The automated crash reports are a “neat way” to gain “passive access” to a machine, the presentation continues. Passive access means that, initially, only data the computer sends out into the Internet is captured and saved, but the computer itself is not yet manipulated. Still, even this passive access to error messages provides valuable insights into problems with a targeted person’s computer and, thus, information on security holes that might be exploitable for planting malware or spyware on the unwitting victim’s computer.

Although the method appears to have little importance in practical terms, the NSA’s agents still seem to enjoy it because it allows them to have a bit of a laugh at the expense of the Seattle-based software giant. In one internal graphic, they replaced the text of Microsoft’s original error message with one of their own reading, “This information may be intercepted by a foreign sigint system to gather detailed information and better exploit your machine.” (“Sigint” stands for “signals intelligence.”)

The article talks about the (limited) value of this information with regard to specific target computers, but I have another question: how valuable would this database be for finding new zero-day Windows vulnerabilities to exploit? Microsoft won’t have the incentive to examine and fix problems until they happen broadly among its user base. The NSA has a completely different incentive structure.

I don’t remember this being discussed back in 2013.

EDITED TO ADD (8/6): Slashdot thread.

Top 10 Most Obvious Hacks of All Time (v0.9)

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/07/top-10-most-obvious-hacks-of-all-time.html

For teaching hacking/cybersecurity, I thought I’d create of the most obvious hacks of all time. Not the best hacks, the most sophisticated hacks, or the hacks with the biggest impact, but the most obvious hacks — ones that even the least knowledgeable among us should be able to understand. Below I propose some hacks that fit this bill, though in no particular order.

The reason I’m writing this is that my niece wants me to teach her some hacking. I thought I’d start with the obvious stuff first.

Shared Passwords

If you use the same password for every website, and one of those websites gets hacked, then the hacker has your password for all your websites. The reason your Facebook account got hacked wasn’t because of anything Facebook did, but because you used the same email-address and password when creating an account on “beagleforums.com”, which got hacked last year.

I’ve heard people say “I’m sure, because I choose a complex password and use it everywhere”. No, this is the very worst thing you can do. Sure, you can the use the same password on all sites you don’t care much about, but for Facebook, your email account, and your bank, you should have a unique password, so that when other sites get hacked, your important sites are secure.

And yes, it’s okay to write down your passwords on paper.

Tools: HaveIBeenPwned.com

PIN encrypted PDFs

My accountant emails PDF statements encrypted with the last 4 digits of my Social Security Number. This is not encryption — a 4 digit number has only 10,000 combinations, and a hacker can guess all of them in seconds.
PIN numbers for ATM cards work because ATM machines are online, and the machine can reject your card after four guesses. PIN numbers don’t work for documents, because they are offline — the hacker has a copy of the document on their own machine, disconnected from the Internet, and can continue making bad guesses with no restrictions.
Passwords protecting documents must be long enough that even trillion upon trillion guesses are insufficient to guess.

Tools: Hashcat, John the Ripper

SQL and other injection

The lazy way of combining websites with databases is to combine user input with an SQL statement. This combines code with data, so the obvious consequence is that hackers can craft data to mess with the code.
No, this isn’t obvious to the general public, but it should be obvious to programmers. The moment you write code that adds unfiltered user-input to an SQL statement, the consequence should be obvious. Yet, “SQL injection” has remained one of the most effective hacks for the last 15 years because somehow programmers don’t understand the consequence.
CGI shell injection is a similar issue. Back in early days, when “CGI scripts” were a thing, it was really important, but these days, not so much, so I just included it with SQL. The consequence of executing shell code should’ve been obvious, but weirdly, it wasn’t. The IT guy at the company I worked for back in the late 1990s came to me and asked “this guy says we have a vulnerability, is he full of shit?”, and I had to answer “no, he’s right — obviously so”.

XSS (“Cross Site Scripting”) [*] is another injection issue, but this time at somebody’s web browser rather than a server. It works because websites will echo back what is sent to them. For example, if you search for Cross Site Scripting with the URL https://www.google.com/search?q=cross+site+scripting, then you’ll get a page back from the server that contains that string. If the string is JavaScript code rather than text, then some servers (thought not Google) send back the code in the page in a way that it’ll be executed. This is most often used to hack somebody’s account: you send them an email or tweet a link, and when they click on it, the JavaScript gives control of the account to the hacker.

Cross site injection issues like this should probably be their own category, but I’m including it here for now.

More: Wikipedia on SQL injection, Wikipedia on cross site scripting.
Tools: Burpsuite, SQLmap

Buffer overflows

In the C programming language, programmers first create a buffer, then read input into it. If input is long than the buffer, then it overflows. The extra bytes overwrite other parts of the program, letting the hacker run code.
Again, it’s not a thing the general public is expected to know about, but is instead something C programmers should be expected to understand. They should know that it’s up to them to check the length and stop reading input before it overflows the buffer, that there’s no language feature that takes care of this for them.
We are three decades after the first major buffer overflow exploits, so there is no excuse for C programmers not to understand this issue.

What makes particular obvious is the way they are wrapped in exploits, like in Metasploit. While the bug itself is obvious that it’s a bug, actually exploiting it can take some very non-obvious skill. However, once that exploit is written, any trained monkey can press a button and run the exploit. That’s where we get the insult “script kiddie” from — referring to wannabe-hackers who never learn enough to write their own exploits, but who spend a lot of time running the exploit scripts written by better hackers than they.

More: Wikipedia on buffer overflow, Wikipedia on script kiddie,  “Smashing The Stack For Fun And Profit” — Phrack (1996)
Tools: bash, Metasploit

SendMail DEBUG command (historical)

The first popular email server in the 1980s was called “SendMail”. It had a feature whereby if you send a “DEBUG” command to it, it would execute any code following the command. The consequence of this was obvious — hackers could (and did) upload code to take control of the server. This was used in the Morris Worm of 1988. Most Internet machines of the day ran SendMail, so the worm spread fast infecting most machines.
This bug was mostly ignored at the time. It was thought of as a theoretical problem, that might only rarely be used to hack a system. Part of the motivation of the Morris Worm was to demonstrate that such problems was to demonstrate the consequences — consequences that should’ve been obvious but somehow were rejected by everyone.

More: Wikipedia on Morris Worm

Email Attachments/Links

I’m conflicted whether I should add this or not, because here’s the deal: you are supposed to click on attachments and links within emails. That’s what they are there for. The difference between good and bad attachments/links is not obvious. Indeed, easy-to-use email systems makes detecting the difference harder.
On the other hand, the consequences of bad attachments/links is obvious. That worms like ILOVEYOU spread so easily is because people trusted attachments coming from their friends, and ran them.
We have no solution to the problem of bad email attachments and links. Viruses and phishing are pervasive problems. Yet, we know why they exist.

Default and backdoor passwords

The Mirai botnet was caused by surveillance-cameras having default and backdoor passwords, and being exposed to the Internet without a firewall. The consequence should be obvious: people will discover the passwords and use them to take control of the bots.
Surveillance-cameras have the problem that they are usually exposed to the public, and can’t be reached without a ladder — often a really tall ladder. Therefore, you don’t want a button consumers can press to reset to factory defaults. You want a remote way to reset them. Therefore, they put backdoor passwords to do the reset. Such passwords are easy for hackers to reverse-engineer, and hence, take control of millions of cameras across the Internet.
The same reasoning applies to “default” passwords. Many users will not change the defaults, leaving a ton of devices hackers can hack.

Masscan and background radiation of the Internet

I’ve written a tool that can easily scan the entire Internet in a short period of time. It surprises people that this possible, but it obvious from the numbers. Internet addresses are only 32-bits long, or roughly 4 billion combinations. A fast Internet link can easily handle 1 million packets-per-second, so the entire Internet can be scanned in 4000 seconds, little more than an hour. It’s basic math.
Because it’s so easy, many people do it. If you monitor your Internet link, you’ll see a steady trickle of packets coming in from all over the Internet, especially Russia and China, from hackers scanning the Internet for things they can hack.
People’s reaction to this scanning is weirdly emotional, taking is personally, such as:
  1. Why are they hacking me? What did I do to them?
  2. Great! They are hacking me! That must mean I’m important!
  3. Grrr! How dare they?! How can I hack them back for some retribution!?

I find this odd, because obviously such scanning isn’t personal, the hackers have no idea who you are.

Tools: masscan, firewalls

Packet-sniffing, sidejacking

If you connect to the Starbucks WiFi, a hacker nearby can easily eavesdrop on your network traffic, because it’s not encrypted. Windows even warns you about this, in case you weren’t sure.

At DefCon, they have a “Wall of Sheep”, where they show passwords from people who logged onto stuff using the insecure “DefCon-Open” network. Calling them “sheep” for not grasping this basic fact that unencrypted traffic is unencrypted.

To be fair, it’s actually non-obvious to many people. Even if the WiFi itself is not encrypted, SSL traffic is. They expect their services to be encrypted, without them having to worry about it. And in fact, most are, especially Google, Facebook, Twitter, Apple, and other major services that won’t allow you to log in anymore without encryption.

But many services (especially old ones) may not be encrypted. Unless users check and verify them carefully, they’ll happily expose passwords.

What’s interesting about this was 10 years ago, when most services which only used SSL to encrypt the passwords, but then used unencrypted connections after that, using “cookies”. This allowed the cookies to be sniffed and stolen, allowing other people to share the login session. I used this on stage at BlackHat to connect to somebody’s GMail session. Google, and other major websites, fixed this soon after. But it should never have been a problem — because the sidejacking of cookies should have been obvious.

Tools: Wireshark, dsniff

Stuxnet LNK vulnerability

Again, this issue isn’t obvious to the public, but it should’ve been obvious to anybody who knew how Windows works.
When Windows loads a .dll, it first calls the function DllMain(). A Windows link file (.lnk) can load icons/graphics from the resources in a .dll file. It does this by loading the .dll file, thus calling DllMain. Thus, a hacker could put on a USB drive a .lnk file pointing to a .dll file, and thus, cause arbitrary code execution as soon as a user inserted a drive.
I say this is obvious because I did this, created .lnks that pointed to .dlls, but without hostile DllMain code. The consequence should’ve been obvious to me, but I totally missed the connection. We all missed the connection, for decades.

Social Engineering and Tech Support [* * *]

After posting this, many people have pointed out “social engineering”, especially of “tech support”. This probably should be up near #1 in terms of obviousness.

The classic example of social engineering is when you call tech support and tell them you’ve lost your password, and they reset it for you with minimum of questions proving who you are. For example, you set the volume on your computer really loud and play the sound of a crying baby in the background and appear to be a bit frazzled and incoherent, which explains why you aren’t answering the questions they are asking. They, understanding your predicament as a new parent, will go the extra mile in helping you, resetting “your” password.

One of the interesting consequences is how it affects domain names (DNS). It’s quite easy in many cases to call up the registrar and convince them to transfer a domain name. This has been used in lots of hacks. It’s really hard to defend against. If a registrar charges only $9/year for a domain name, then it really can’t afford to provide very good tech support — or very secure tech support — to prevent this sort of hack.

Social engineering is such a huge problem, and obvious problem, that it’s outside the scope of this document. Just google it to find example after example.

A related issue that perhaps deserves it’s own section is OSINT [*], or “open-source intelligence”, where you gather public information about a target. For example, on the day the bank manager is out on vacation (which you got from their Facebook post) you show up and claim to be a bank auditor, and are shown into their office where you grab their backup tapes. (We’ve actually done this).

More: Wikipedia on Social Engineering, Wikipedia on OSINT, “How I Won the Defcon Social Engineering CTF” — blogpost (2011), “Questioning 42: Where’s the Engineering in Social Engineering of Namespace Compromises” — BSidesLV talk (2016)

Blue-boxes (historical) [*]

Telephones historically used what we call “in-band signaling”. That’s why when you dial on an old phone, it makes sounds — those sounds are sent no differently than the way your voice is sent. Thus, it was possible to make tone generators to do things other than simply dial calls. Early hackers (in the 1970s) would make tone-generators called “blue-boxes” and “black-boxes” to make free long distance calls, for example.

These days, “signaling” and “voice” are digitized, then sent as separate channels or “bands”. This is call “out-of-band signaling”. You can’t trick the phone system by generating tones. When your iPhone makes sounds when you dial, it’s entirely for you benefit and has nothing to do with how it signals the cell tower to make a call.

Early hackers, like the founders of Apple, are famous for having started their careers making such “boxes” for tricking the phone system. The problem was obvious back in the day, which is why as the phone system moves from analog to digital, the problem was fixed.

More: Wikipedia on blue box, Wikipedia article on Steve Wozniak.

Thumb drives in parking lots [*]

A simple trick is to put a virus on a USB flash drive, and drop it in a parking lot. Somebody is bound to notice it, stick it in their computer, and open the file.

This can be extended with tricks. For example, you can put a file labeled “third-quarter-salaries.xlsx” on the drive that required macros to be run in order to open. It’s irresistible to other employees who want to know what their peers are being paid, so they’ll bypass any warning prompts in order to see the data.

Another example is to go online and get custom USB sticks made printed with the logo of the target company, making them seem more trustworthy.

We also did a trick of taking an Adobe Flash game “Punch the Monkey” and replaced the monkey with a logo of a competitor of our target. They now only played the game (infecting themselves with our virus), but gave to others inside the company to play, infecting others, including the CEO.

Thumb drives like this have been used in many incidents, such as Russians hacking military headquarters in Afghanistan. It’s really hard to defend against.

More: “Computer Virus Hits U.S. Military Base in Afghanistan” — USNews (2008), “The Return of the Worm That Ate The Pentagon” — Wired (2011), DoD Bans Flash Drives — Stripes (2008)

Googling [*]

Search engines like Google will index your website — your entire website. Frequently companies put things on their website without much protection because they are nearly impossible for users to find. But Google finds them, then indexes them, causing them to pop up with innocent searches.
There are books written on “Google hacking” explaining what search terms to look for, like “not for public release”, in order to find such documents.

More: Wikipedia entry on Google Hacking, “Google Hacking” book.

URL editing [*]

At the top of every browser is what’s called the “URL”. You can change it. Thus, if you see a URL that looks like this:

http://www.example.com/documents?id=138493

Then you can edit it to see the next document on the server:

http://www.example.com/documents?id=138494

The owner of the website may think they are secure, because nothing points to this document, so the Google search won’t find it. But that doesn’t stop a user from manually editing the URL.
An example of this is a big Fortune 500 company that posts the quarterly results to the website an hour before the official announcement. Simply editing the URL from previous financial announcements allows hackers to find the document, then buy/sell the stock as appropriate in order to make a lot of money.
Another example is the classic case of Andrew “Weev” Auernheimer who did this trick in order to download the account email addresses of early owners of the iPad, including movie stars and members of the Obama administration. It’s an interesting legal case because on one hand, techies consider this so obvious as to not be “hacking”. On the other hand, non-techies, especially judges and prosecutors, believe this to be obviously “hacking”.

DDoS, spoofing, and amplification [*]

For decades now, online gamers have figured out an easy way to win: just flood the opponent with Internet traffic, slowing their network connection. This is called a DoS, which stands for “Denial of Service”. DoSing game competitors is often a teenager’s first foray into hacking.
A variant of this is when you hack a bunch of other machines on the Internet, then command them to flood your target. (The hacked machines are often called a “botnet”, a network of robot computers). This is called DDoS, or “Distributed DoS”. At this point, it gets quite serious, as instead of competitive gamers hackers can take down entire businesses. Extortion scams, DDoSing websites then demanding payment to stop, is a common way hackers earn money.
Another form of DDoS is “amplification”. Sometimes when you send a packet to a machine on the Internet it’ll respond with a much larger response, either a very large packet or many packets. The hacker can then send a packet to many of these sites, “spoofing” or forging the IP address of the victim. This causes all those sites to then flood the victim with traffic. Thus, with a small amount of outbound traffic, the hacker can flood the inbound traffic of the victim.
This is one of those things that has worked for 20 years, because it’s so obvious teenagers can do it, yet there is no obvious solution. President Trump’s executive order of cyberspace specifically demanded that his government come up with a report on how to address this, but it’s unlikely that they’ll come up with any useful strategy.

More: Wikipedia on DDoS, Wikipedia on Spoofing

Conclusion

Tweet me (@ErrataRob) your obvious hacks, so I can add them to the list.

Apple Bans VPNs From App Store in China

Post Syndicated from Ernesto original https://torrentfreak.com/apple-bans-vpns-from-app-store-in-china-170729/

Apple is known to have a rigorous app-review policy.

Over the past several years, dozens of apps have been rejected from the App Store because they mention the word BitTorrent, for example.

The mere association with piracy is good enough to warrant a ban. This policy is now expanding to the privacy-sphere as well, at least in China.

It is no secret that the Chinese Government is preventing users from accessing certain sites and services. The so-called ‘Great Firewall’ works reasonably well, but can be circumvented through VPN services and other encryption tools.

These tools are a thorn in the side of Chinese authorities, which are now receiving help from Apple to limit their availability.

Over the past few hours, Apple has removed many of the most-used VPN applications from the Chinese app store. In a short email, VPN providers are informed that VPN applications are considered illegal in China.

“We are writing to notify you that your application will be removed from the China App Store because it includes content that is illegal in China, which is not in compliance with the App Store Review Guidelines,” Apple informed the affected VPNs.

Apple’s email to VPN providers

VPN providers and users are complaining bitterly about the rigorous action. However, it doesn’t come as a complete surprise. Over the past few months there have been various signals that the Chinese Government would crack down on non-authorized VPN providers.

In January, a notice published by China’s Ministry of Industry and Information Technology said that the government had launched a 14-month campaign to crack down on local ‘unauthorized’ Internet platforms.

This essentially means that all VPN services have to be pre-approved by the Government if they want to operate there.

Earlier this month Bloomberg broke the news that China’s Government had ordered telecommunications carriers to block individuals’ access to VPNs. The Chinese Government denied that this was the case, but it’s clear that these services remain a high-profile target.

Thanks to Apple, China’s Government no longer has to worry about iOS users having easy access to the most popular VPN applications. Those users who search the local app store for “VPN” still see plenty of results, but, ironically, many of these applications are fake.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Which Domain Names Are Safe From Copyright Bullies?

Post Syndicated from Ernesto original https://torrentfreak.com/which-domain-names-are-safe-from-copyright-bullies-170728/

There are plenty options for copyright holders to frustrate the operation of pirate sites, but one of the most effective is to attack their domain names.

The strategy has been deployed most famously against The Pirate Bay. Over the past couple of years, the site has lost more than a handful following copyright holder complaints.

While less public, hundreds of smaller sites have suffered the same fate. Sometimes these sites are clear infringers, but in other cases it’s less obvious. In these instances, a simple complaint can also be enough to have a domain name suspended.

Electronic Frontier Foundation (EFF) and Public Knowledge address this ‘copyright bullying’ problem in a newly published whitepaper. According to the digital rights groups, site owners should pick their domain names carefully, and go for a registry that shields website owners from this type of abuse.

“It turns out that not every top-level domain is created equal when it comes to protecting the domain holder’s rights. Depending on where you register your domain, a rival, troll, or officious regulator who doesn’t like what you’re doing with it could wrongly take it away,” the groups warn.

The whitepaper includes a detailed analysis of the policies of various domain name registries. For each, it lists the home country, under which conditions domain names are removed, and whether the WHOIS details of registrants are protected.

When it comes to “copyright bullies,” the digital rights groups highlight the MPAA’s voluntary agreements with the Radix and Donuts registries. The agreement allows the MPAA to report infringing sites directly to the registry. These can then be removed after a careful review but without a court order.

“Our whitepaper illustrates why remedies for copyright infringement on the Internet should not come from the domain name system, and in particular should not be wielded by commercial actors in an unaccountable process. Organizations such as the MPAA are not known for advancing a balanced approach to copyright enforcement,” the EFF explains.

While EFF and Public Knowledge don’t recommend any TLDs in particular, they do signal some that site owners may want to avoid. The Radix and Donuts domain names are obviously not the best choice, in this regard.

“To avoid having your website taken down by your domain registry in response to a copyright complaint, our whitepaper sets out a number of options, including registering in a domain whose registry requires a court order before it will take down a domain, or at the very least one that doesn’t have a special arrangement with the MPAA or another special interest for the streamlined takedown of domains,” the groups write.

Aside from the information gathered in the whitepaper, The Pirate Bay itself has also proven to be an excellent test case of which domain names are most resistant to copyright holder complaints.

In 2015, the notorious torrent site found out that exotic domain names are not always the best option after losing its .GS, .LA, .VG, .AM, .MN, and .GD TLDs in a matter of months. The good old .ORG is still up and running as of today, however, despite being operated by a United States-based registry.

EFF and Public knowledge’s full whitepaper is available here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.