Tag Archives: messaging

Greater Transparency into Actions AWS Services Perform on Your Behalf by Using AWS CloudTrail

Post Syndicated from Ujjwal Pugalia original https://aws.amazon.com/blogs/security/get-greater-transparency-into-actions-aws-services-perform-on-your-behalf-by-using-aws-cloudtrail/

To make managing your AWS account easier, some AWS services perform actions on your behalf, including the creation and management of AWS resources. For example, AWS Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring. To make these AWS actions more transparent, AWS adds an AWS Identity and Access Management (IAM) service-linked roles to your account for each linked service you use. Service-linked roles let you view all actions an AWS service performs on your behalf by using AWS CloudTrail logs. This helps you monitor and audit the actions AWS services perform on your behalf. No additional actions are required from you and you can continue using AWS services the way you do today.

To learn more about which AWS services use service-linked roles and log actions on your behalf to CloudTrail, see AWS Services That Work with IAM. Over time, more AWS services will support service-linked roles. For more information about service-linked roles, see Role Terms and Concepts.

In this blog post, I demonstrate how to view CloudTrail logs so that you can more easily monitor and audit AWS services performing actions on your behalf. First, I show how AWS creates a service-linked role in your account automatically when you configure an AWS service that supports service-linked roles. Next, I show how you can view the policies of a service-linked role that grants an AWS service permission to perform actions on your behalf. Finally, I  use the configured AWS service to perform an action and show you how the action appears in your CloudTrail logs.

How AWS creates a service-linked role in your account automatically

I will use Amazon Lex as the AWS service that performs actions on your behalf for this post. You can use Amazon Lex to create chatbots that allow for highly engaging conversational experiences through voice and text. You also can use chatbots on mobile devices, web browsers, and popular chat platform channels such as Slack. Amazon Lex uses Amazon Polly on your behalf to synthesize speech that sounds like a human voice.

Amazon Lex uses two IAM service-linked roles:

  • AWSServiceRoleForLexBots — Amazon Lex uses this service-linked role to invoke Amazon Polly to synthesize speech responses for your chatbot.
  • AWSServiceRoleForLexChannels — Amazon Lex uses this service-linked role to post text to your chatbot when managing channels such as Slack.

You don’t need to create either of these roles manually. When you create your first chatbot using the Amazon Lex console, Amazon Lex creates the AWSServiceRoleForLexBots role for you. When you first associate a chatbot with a messaging channel, Amazon Lex creates the AWSServiceRoleForLexChannels role in your account.

1. Start configuring the AWS service that supports service-linked roles

Navigate to the Amazon Lex console, and choose Get Started to navigate to the Create your Lex bot page. For this example, I choose a sample chatbot called OrderFlowers. To learn how to create a custom chatbot, see Create a Custom Amazon Lex Bot.

Screenshot of making the choice to create an OrderFlowers chatbot

2. Complete the configuration for the AWS service

When you scroll down, you will see the settings for the OrderFlowers chatbot. Notice the field for the IAM role with the value, AWSServiceRoleForLexBots. This service-linked role is “Automatically created on your behalf.” After you have entered all details, choose Create to build your sample chatbot.

Screenshot of the automatically created service-linked role

AWS has created the AWSServiceRoleForLexBots service-linked role in your account. I will return to using the chatbot later in this post when I discuss how Amazon Lex performs actions on your behalf and how CloudTrail logs these actions. First, I will show how you can view the permissions for the AWSServiceRoleForLexBots service-linked role by using the IAM console.

How to view actions in the IAM console that AWS services perform on your behalf

When you configure an AWS service that supports service-linked roles, AWS creates a service-linked role in your account automatically. You can view the service-linked role by using the IAM console.

1. View the AWSServiceRoleForLexBots service-linked role on the IAM console

Go to the IAM console, and choose AWSServiceRoleForLexBots on the Roles page. You can confirm that this role is a service-linked role by viewing the Trusted entities column.

Screenshot of the service-linked role

2.View the trusted entities that can assume the AWSServiceRoleForLexBots service-linked role

Choose the Trust relationships tab on the AWSServiceRoleForLexBots role page. You can view the trusted entities that can assume the AWSServiceRoleForLexBots service-linked role to perform actions on your behalf. In this example, the trusted entity is lex.amazonaws.com.

Screenshot of the trusted entities that can assume the service-linked role

3. View the policy attached to the AWSServiceRoleForLexBots service-linked role

Choose AmazonLexBotPolicy on the Permissions tab to view the policy attached to the AWSServiceRoleForLexBots service-linked role. You can view the policy summary to see that AmazonLexBotPolicy grants permission to Amazon Lex to use Amazon Polly.

Screenshot showing that AmazonLexBotPolicy grants permission to Amazon Lex to use Amazon Polly

4. View the actions that the service-linked role grants permissions to use

Choose Polly to view the action, SynthesizeSpeech, that the AmazonLexBotPolicy grants permission to Amazon Lex to perform on your behalf. Amazon Lex uses this permission to synthesize speech responses for your chatbot. I show later in this post how you can monitor this SynthesizeSpeech action in your CloudTrail logs.

Screenshot showing the the action, SynthesizeSpeech, that the AmazonLexBotPolicy grants permission to Amazon Lex to perform on your behalf

Now that I know the trusted entity and the policy attached to the service-linked role, let’s go back to the chatbot I created earlier and see how CloudTrail logs the actions that Amazon Lex performs on my behalf.

How to use CloudTrail to view actions that AWS services perform on your behalf

As discussed already, I created an OrderFlowers chatbot on the Amazon Lex console. I will use the chatbot and display how the AWSServiceRoleForLexBots service-linked role helps me track actions in CloudTrail. First, though, I must have an active CloudTrail trail created that stores the logs in an Amazon S3 bucket. I will use a trail called TestTrail and an S3 bucket called account-ids-slr.

1. Use the Amazon Lex chatbot via the Amazon Lex console

In Step 2 in the first section of this post, when I chose Create, Amazon Lex built the OrderFlowers chatbot. After the chatbot was built, the right pane showed that a Test Bot was created. Now, I choose the microphone symbol in the right pane and provide voice input to test the OrderFlowers chatbot. In this example, I tell the chatbot, “I would like to order some flowers.” The bot replies to me by asking, “What type of flowers would you like to order?”

Screenshot of voice input to test the OrderFlowers chatbot

When the chatbot replies using voice, Amazon Lex uses Amazon Polly to synthesize speech from text to voice. Amazon Lex assumes the AWSServiceRoleForLexBots service-linked role to perform the SynthesizeSpeech action.

2. Check CloudTrail to view actions performed on your behalf

Now that I have created the chatbot, let’s see which actions were logged in CloudTrail. Choose CloudTrail from the Services drop-down menu to reach the CloudTrail console. Choose Trails and choose the S3 bucket in which you are storing your CloudTrail logs.

Screenshot of the TestTrail trail

In the S3 bucket, you will find log entries for the SynthesizeSpeech event. This means that CloudTrail logged the action when Amazon Lex assumed the AWSServiceRoleForLexBots service-linked role to invoke Amazon Polly to synthesize speech responses for your chatbot. You can monitor and audit this invocation, and it provides you with transparency into Amazon Polly’s SynthesizeSpeech action that Amazon Lex invoked on your behalf. The applicable CloudTrail log section follows and I have emphasized the key lines.

{  
         "eventVersion":"1.05",
         "userIdentity":{  
           "type":"AssumedRole",
            "principalId":"{principal-id}:OrderFlowers",
            "arn":"arn:aws:sts::{account-id}:assumed-role/AWSServiceRoleForLexBots/OrderFlowers",
            "accountId":"{account-id}",
            "accessKeyId":"{access-key-id}",
            "sessionContext":{  
               "attributes":{  
                  "mfaAuthenticated":"false",
                  "creationDate":"2017-09-17T17:30:05Z"
               },
               "sessionIssuer":{  
                  "type":"Role",
                  "principalId":"{principal-id}",
                  "arn":"arn:aws:iam:: {account-id}:role/aws-service-role/lex.amazonaws.com/AWSServiceRoleForLexBots",
                  "accountId":"{account-id",
                  "userName":"AWSServiceRoleForLexBots"
               }
            },
            "invokedBy":"lex.amazonaws.com"
         },
         "eventTime":"2017-09-17T17:30:05Z",
         "eventSource":"polly.amazonaws.com",
         "eventName":"SynthesizeSpeech",
         "awsRegion":"us-east-1",
         "sourceIPAddress":"lex.amazonaws.com",
         "userAgent":"lex.amazonaws.com",
         "requestParameters":{  
            "outputFormat":"mp3",
            "textType":"text",
            "voiceId":"Salli",
            "text":"**********"
         },
         "responseElements":{  
            "requestCharacters":45,
            "contentType":"audio/mpeg"
         },
         "requestID":"{request-id}",
         "eventID":"{event-id}",
         "eventType":"AwsApiCall",
         "recipientAccountId":"{account-id}"
      }

Conclusion

Service-linked roles make it easier for you to track and view actions that linked AWS services perform on your behalf by using CloudTrail. When an AWS service supports service-linked roles to enable this additional logging, you will see a service-linked role added to your account.

If you have comments about this post, submit a comment in the “Comments” section below. If you have questions about working with service-linked roles, start a new thread on the IAM forum or contact AWS Support.

– Ujjwal

Simplify Your Jenkins Builds with AWS CodeBuild

Post Syndicated from Paul Roberts original https://aws.amazon.com/blogs/devops/simplify-your-jenkins-builds-with-aws-codebuild/

Jeff Bezos famously said, “There’s a lot of undifferentiated heavy lifting that stands between your idea and that success.” He went on to say, “…70% of your time, energy, and dollars go into the undifferentiated heavy lifting and only 30% of your energy, time, and dollars gets to go into the core kernel of your idea.”

If you subscribe to this maxim, you should not be spending valuable time focusing on operational issues related to maintaining the Jenkins build infrastructure. Companies such as Riot Games have over 1.25 million builds per year and have written several lengthy blog posts about their experiences designing a complex, custom Docker-powered Jenkins build farm. Dealing with Jenkins slaves at scale is a job in itself and Riot has engineers focused on managing the build infrastructure.

Typical Jenkins Build Farm

 

As with all technology, the Jenkins build farm architectures have evolved. Today, instead of manually building your own container infrastructure, there are Jenkins Docker plugins available to help reduce the operational burden of maintaining these environments. There is also a community-contributed Amazon EC2 Container Service (Amazon ECS) plugin that helps remove some of the overhead, but you still need to configure and manage the overall Amazon ECS environment.

There are various ways to create and manage your Jenkins build farm, but there has to be a way that significantly reduces your operational overhead.

Introducing AWS CodeBuild

AWS CodeBuild is a fully managed build service that removes the undifferentiated heavy lifting of provisioning, managing, and scaling your own build servers. With CodeBuild, there is no software to install, patch, or update. CodeBuild scales up automatically to meet the needs of your development teams. In addition, CodeBuild is an on-demand service where you pay as you go. You are charged based only on the number of minutes it takes to complete your build.

One AWS customer, Recruiterbox, helps companies hire simply and predictably through their software platform. Two years ago, they began feeling the operational pain of maintaining their own Jenkins build farms. They briefly considered moving to Amazon ECS, but chose an even easier path forward instead. Recuiterbox transitioned to using Jenkins with CodeBuild and are very happy with the results. You can read more about their journey here.

Solution Overview: Jenkins and CodeBuild

To remove the heavy lifting from managing your Jenkins build farm, AWS has developed a Jenkins AWS CodeBuild plugin. After the plugin has been enabled, a developer can configure a Jenkins project to pick up new commits from their chosen source code repository and automatically run the associated builds. After the build is successful, it will create an artifact that is stored inside an S3 bucket that you have configured. If an error is detected somewhere, CodeBuild will capture the output and send it to Amazon CloudWatch logs. In addition to storing the logs on CloudWatch, Jenkins also captures the error so you do not have to go hunting for log files for your build.

 

AWS CodeBuild with Jenkins Plugin

 

The following example uses AWS CodeCommit (Git) as the source control management (SCM) and Amazon S3 for build artifact storage. Logs are stored in CloudWatch. A development pipeline that uses Jenkins with CodeBuild plugin architecture looks something like this:

 

AWS CodeBuild Diagram

Initial Solution Setup

To keep this blog post succinct, I assume that you are using the following components on AWS already and have applied the appropriate IAM policies:

·         AWS CodeCommit repo.

·         Amazon S3 bucket for CodeBuild artifacts.

·         SNS notification for text messaging of the Jenkins admin password.

·         IAM user’s key and secret.

·         A role that has a policy with these permissions. Be sure to edit the ARNs with your region, account, and resource name. Use this role in the AWS CloudFormation template referred to later in this post.

 

Jenkins Installation with CodeBuild Plugin Enabled

To make the integration with Jenkins as frictionless as possible, I have created an AWS CloudFormation template here: https://s3.amazonaws.com/proberts-public/jenkins.yaml. Download the template, sign in the AWS CloudFormation console, and then use the template to create a stack.

 

CloudFormation Inputs

Jenkins Project Configuration

After the stack is complete, log in to the Jenkins EC2 instance using the user name “admin” and the password sent to your mobile device. Now that you have logged in to Jenkins, you need to create your first project. Start with a Freestyle project and configure the parameters based on your CodeBuild and CodeCommit settings.

 

AWS CodeBuild Plugin Configuration in Jenkins

 

Additional Jenkins AWS CodeBuild Plugin Configuration

 

After you have configured the Jenkins project appropriately you should be able to check your build status on the Jenkins polling log under your project settings:

 

Jenkins Polling Log

 

Now that Jenkins is polling CodeCommit, you can check the CodeBuild dashboard under your Jenkins project to confirm your build was successful:

Jenkins AWS CodeBuild Dashboard

Wrapping Up

In a matter of minutes, you have been able to provision Jenkins with the AWS CodeBuild plugin. This will greatly simplify your build infrastructure management. Now kick back and relax while CodeBuild does all the heavy lifting!


About the Author

Paul Roberts is a Strategic Solutions Architect for Amazon Web Services. When he is not working on Serverless, DevOps, or Artificial Intelligence, he is often found in Lake Tahoe exploring the various mountain ranges with his family.

New Network Load Balancer – Effortless Scaling to Millions of Requests per Second

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-network-load-balancer-effortless-scaling-to-millions-of-requests-per-second/

Elastic Load Balancing (ELB)) has been an important part of AWS since 2009, when it was launched as part of a three-pack that also included Auto Scaling and Amazon CloudWatch. Since that time we have added many features, and also introduced the Application Load Balancer. Designed to support application-level, content-based routing to applications that run in containers, Application Load Balancers pair well with microservices, streaming, and real-time workloads.

Over the years, our customers have used ELB to support web sites and applications that run at almost any scale — from simple sites running on a T2 instance or two, all the way up to complex applications that run on large fleets of higher-end instances and handle massive amounts of traffic. Behind the scenes, ELB monitors traffic and automatically scales to meet demand. This process, which includes a generous buffer of headroom, has become quicker and more responsive over the years and works well even for our customers who use ELB to support live broadcasts, “flash” sales, and holidays. However, in some situations such as instantaneous fail-over between regions, or extremely spiky workloads, we have worked with our customers to pre-provision ELBs in anticipation of a traffic surge.

New Network Load Balancer
Today we are introducing the new Network Load Balancer (NLB). It is designed to handle tens of millions of requests per second while maintaining high throughput at ultra low latency, with no effort on your part. The Network Load Balancer is API-compatible with the Application Load Balancer, including full programmatic control of Target Groups and Targets. Here are some of the most important features:

Static IP Addresses – Each Network Load Balancer provides a single IP address for each VPC subnet in its purview. If you have targets in a subnet in us-west-2a and other targets in a subnet in us-west-2c, NLB will create and manage two IP addresses (one per subnet); connections to that IP address will spread traffic across the instances in the subnet. You can also specify an existing Elastic IP for each subnet for even greater control. With full control over your IP addresses, Network Load Balancer can be used in situations where IP addresses need to be hard-coded into DNS records, customer firewall rules, and so forth.

Zonality – The IP-per-subnet feature reduces latency with improved performance, improves availability through isolation and fault tolerance and makes the use of Network Load Balancers transparent to your client applications. Network Load Balancers also attempt to route a series of requests from a particular source to targets in a single subnet while still allowing automatic failover.

Source Address Preservation – With Network Load Balancer, the original source IP address and source ports for the incoming connections remain unmodified, so application software need not support X-Forwarded-For, proxy protocol, or other workarounds. This also means that normal firewall rules, including VPC Security Groups, can be used on targets.

Long-running Connections – NLB handles connections with built-in fault tolerance, and can handle connections that are open for months or years, making them a great fit for IoT, gaming, and messaging applications.

Failover – Powered by Route 53 health checks, NLB supports failover between IP addresses within and across regions.

Creating a Network Load Balancer
I can create a Network Load Balancer opening up the EC2 Console, selecting Load Balancers, and clicking on Create Load Balancer:

I choose Network Load Balancer and click on Create, then enter the details. I can choose an Elastic IP address for each subnet in the target VPC and I can tag the Network Load Balancer:

Then I click on Configure Routing and create a new target group. I enter a name, and then choose the protocol and port. I can also set up health checks that go to the traffic port or to the alternate of my choice:

Then I click on Register Targets and the EC2 instances that will receive traffic, and click on Add to registered:

I make sure that everything looks good and then click on Create:

The state of my new Load Balancer is provisioning, switching to active within a minute or so:

For testing purposes, I simply grab the DNS name of the Load Balancer from the console (in practice I would use Amazon Route 53 and a more friendly name):

Then I sent it a ton of traffic (I intended to let it run for just a second or two but got distracted and it created a huge number of processes, so this was a happy accident):

$ while true;
> do
>   wget http://nlb-1-6386cc6bf24701af.elb.us-west-2.amazonaws.com/phpinfo2.php &
> done

A more disciplined test would use a tool like Bees with Machine Guns, of course!

I took a quick break to let some traffic flow and then checked the CloudWatch metrics for my Load Balancer, finding that it was able to handle the sudden onslaught of traffic with ease:

I also looked at my EC2 instances to see how they were faring under the load (really well, it turns out):

It turns out that my colleagues did run a more disciplined test than I did. They set up a Network Load Balancer and backed it with an Auto Scaled fleet of EC2 instances. They set up a second fleet composed of hundreds of EC2 instances, each running Bees with Machine Guns and configured to generate traffic with highly variable request and response sizes. Beginning at 1.5 million requests per second, they quickly turned the dial all the way up, reaching over 3 million requests per second and 30 Gbps of aggregate bandwidth before maxing out their test resources.

Choosing a Load Balancer
As always, you should consider the needs of your application when you choose a load balancer. Here are some guidelines:

Network Load Balancer (NLB) – Ideal for load balancing of TCP traffic, NLB is capable of handling millions of requests per second while maintaining ultra-low latencies. NLB is optimized to handle sudden and volatile traffic patterns while using a single static IP address per Availability Zone.

Application Load Balancer (ALB) – Ideal for advanced load balancing of HTTP and HTTPS traffic, ALB provides advanced request routing that supports modern application architectures, including microservices and container-based applications.

Classic Load Balancer (CLB) – Ideal for applications that were built within the EC2-Classic network.

For a side-by-side feature comparison, see the Elastic Load Balancer Details table.

If you are currently using a Classic Load Balancer and would like to migrate to a Network Load Balancer, take a look at our new Load Balancer Copy Utility. This Python tool will help you to create a Network Load Balancer with the same configuration as an existing Classic Load Balancer. It can also register your existing EC2 instances with the new load balancer.

Pricing & Availability
Like the Application Load Balancer, pricing is based on Load Balancer Capacity Units, or LCUs. Billing is $0.006 per LCU, based on the highest value seen across the following dimensions:

  • Bandwidth – 1 GB per LCU.
  • New Connections – 800 per LCU.
  • Active Connections – 100,000 per LCU.

Most applications are bandwidth-bound and should see a cost reduction (for load balancing) of about 25% when compared to Application or Classic Load Balancers.

Network Load Balancers are available today in all AWS commercial regions except China (Beijing), supported by AWS CloudFormation, Auto Scaling, and Amazon ECS.

Jeff;

 

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.

China Says It Will “Severely Strike” Websites Involved in Piracy

Post Syndicated from Andy original https://torrentfreak.com/china-says-it-will-severely-strike-websites-involved-in-piracy-170729/

When it comes to the protection of intellectual property, China is often viewed as one of the world’s leading scofflaws. Everything is copied in the country, from designer watches to cars. Not even major landmarks can escape the replica treatment.

In more recent times, however, there have been signs that China might be at least warming to the idea that IP protection should be given more priority.

For example, every few months authorities announce a new crackdown on Internet piracy, such as the “Jian Wang 2016” program which shuttered 290 piracy websites in the final six months of last year.

Maintaining the same naming convention, this week China’s National Copyright Administration revealed the new “Jian Wang 2017” anti-piracy program. During a meeting in Beijing attended by other state bodies, copyright groups, rights organizations, and representatives from the news media, the administration detailed its latest plans.

The anti-piracy program will focus on protecting the copyrights of the film, television, and news industries in China. Infringing websites, e-commerce and cloud storage services, social networks, plus mobile Internet applications will all be put under the spotlight, with authorities investigating and prosecuting major cases.

The program, which will run for the next four months, has a mission to improve compliance in three key areas.

The first aims to assist the film and TV industries by cracking down on ‘pirate’ websites, the unlawful use of file-sharing software, plus “forum communities and other channels that supply infringing film and television works.”

Also on the cards is a blitz against users of the hugely popular social media and instant messaging app, WeChat.

Released in 2011, WeChat now has more than 930 million users, some of which use the platform to republish news articles without permission from creators. Chinese authorities want to reduce this activity, noting that too many articles are stripped from their sources and reproduced on personal blogs and similar platforms.

The second area for attention is the booming market for pirate apps. Chinese authorities say that cracked app stores and the software they provide are contributing to a huge rise in the unlawful spread of films, TV shows, music, news and other literature. Set-top boxes that utilize such apps will also be targeted in the crackdown.

Finally, there will be a “strengthening of copyright supervision” on large-scale e-commerce platforms that supply audio and video products, eBooks, and other publications. Cloud storage platforms will also be subjected to additional scrutiny, as these are often used to share copyright works without permission.

What kind of effect the program will have on overall copyrighted content availability will remain to be seen, but if previous patterns are maintained, the National Copyright Administration should reveal the results of its blitz in December.

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

Top Ten Ways to Protect Yourself Against Phishing Attacks

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/top-ten-ways-protect-phishing-attacks/

It’s hard to miss the increasing frequency of phishing attacks in the news. Earlier this year, a major phishing attack targeted Google Docs users, and attempted to compromise at least one million Google Docs accounts. Experts say the “phish” was convincing and sophisticated, and even people who thought they would never be fooled by a phishing attack were caught in its net.

What is phishing?

Phishing attacks use seemingly trustworthy but malicious emails and websites to obtain your personal account or banking information. The attacks are cunning and highly effective because they often appear to come from an organization or business you actually use. The scam comes into play by tricking you into visiting a website you believe belongs to the trustworthy organization, but in fact is under the control of the phisher attempting to extract your private information.

Phishing attacks are once again in the news due to a handful of high profile ransomware incidents. Ransomware invades a user’s computer, encrypts their data files, and demands payment to decrypt the files. Ransomware most often makes its way onto a user’s computer through a phishing exploit, which gives the ransomware access to the user’s computer.

The best strategy against phishing is to scrutinize every email and message you receive and never to get caught. Easier said than done—even smart people sometimes fall victim to a phishing attack. To minimize the damage in an event of a phishing attack, backing up your data is the best ultimate defense and should be part of your anti-phishing and overall anti-malware strategy.

How do you recognize a phishing attack?

A phishing attacker may send an email seemingly from a reputable credit card company or financial institution that requests account information, often suggesting that there is a problem with your account. When users respond with the requested information, attackers can use it to gain access to the accounts.

The image below is a mockup of how a phishing attempt might appear. In this example, courtesy of Wikipedia, the bank is fictional, but in a real attempt the sender would use an actual bank, perhaps even the bank where the targeted victim does business. The sender is attempting to trick the recipient into revealing confidential information by getting the victim to visit the phisher’s website. Note the misspelling of the words “received” and “discrepancy” as recieved and discrepency. Misspellings sometimes are indications of a phishing attack. Also note that although the URL of the bank’s webpage appears to be legitimate, the hyperlink would actually take you to the phisher’s webpage, which would be altogether different from the URL displayed in the message.

By Andrew Levine – en:Image:PhishingTrustedBank.png, Public Domain, https://commons.wikimedia.org/w/index.php?curid=549747

Top ten ways to protect yourself against phishing attacks

  1. Always think twice when presented with a link in any kind of email or message before you click on it. Ask yourself whether the sender would ask you to do what it is requesting. Most banks and reputable service providers won’t ask you to reveal your account information or password via email. If in doubt, don’t use the link in the message and instead open a new webpage and go directly to the known website of the organization. Sign in to the site in the normal manner to verify that the request is legitimate.
  2. A good precaution is to always hover over a link before clicking on it and observe the status line in your browser to verify that the link in the text and the destination link are in fact the same.
  3. Phishers are clever, and they’re getting better all the time, and you might be fooled by a simple ruse to make you think the link is one you recognize. Links can have hard-to-detect misspellings that would result in visiting a site very different than what you expected.
  4. Be wary even of emails and message from people you know. It’s very easy to spoof an email so it appears to come from someone you know, or to create a URL that appears to be legitimate, but isn’t.

For example, let’s say that you work for roughmedia.com and you get an email from Chuck in accounting ([email protected]) that has an attachment for you, perhaps a company form you need to fill out. You likely wouldn’t notice in the sender address that the phisher has replaced the “m” in media with an “r” and an “n” that look very much like an “m.” You think it’s good old Chuck in finance and it’s actually someone “phishing” for you to open the attachment and infect your computer. This type of attack is known as “spear phishing” because it’s targeted at a specific individual and is using social engineering—specifically familiarity with the sender—as part of the scheme to fool you into trusting the attachment. This technique is by far the most successful on the internet today. (This example is based on Gimlet Media’s Reply All Podcast Episode, “What Kind of Idiot Gets Phished?“)

  1. Use anti-malware software, but don’t rely on it to catch all attacks. Phishers change their approach often to keep ahead of the software attack detectors.
  2. If you are asked to enter any valuable information, only do so if you’re on a secure connection. Look for the “https” prefix before the site URL, indicating the site is employing SSL (Secure Socket Layer). If there is no “s” after “http,” it’s best not to enter any confidential information.
By Fabio Lanari – Internet1.jpg by Rock1997 modified., GFDL, https://commons.wikimedia.org/w/index.php?curid=20995390
  1. Avoid logging in to online banks and similar services via public Wi-Fi networks. Criminals can compromise open networks with man-in-the-middle attacks that capture your information or spoof website addresses over the connection and redirect you to a fake page they control.
  2. Email, instant messaging, and gaming social channels are all possible vehicles to deliver phishing attacks, so be vigilant!
  3. Lay the foundation for a good defense by choosing reputable tech vendors and service providers that respect your privacy and take steps to protect your data. At Backblaze, we have full-time security teams constantly looking for ways to improve our security.
  4. When it is available, always take advantage of multi-factor verification to protect your accounts. The standard categories used for authentication are 1) something you know (e.g. your username and password), 2) something you are (e.g. your fingerprint or retina pattern), and 3) something you have (e.g. an authenticator app on your smartphone). An account that allows only a single factor for authentication is more susceptible to hacking than one that supports multiple factors. Backblaze supports multi-factor authentication to protect customer accounts.

Be a good internet citizen, and help reduce phishing and other malware attacks by notifying the organization being impersonated in the phishing attempt, or by forwarding suspicious messages to the Federal Trade Commission at [email protected]. Some email clients and services, such as Microsoft Outlook and Google Gmail, give you the ability to easily report suspicious emails. Phishing emails misrepresenting Apple can be reported to [email protected].

Backing up your data is an important part of a strong defense against phishing and other malware

The best way to avoid becoming a victim is to be vigilant against suspicious messages and emails, but also to assume that no matter what you do, it is very possible that your system will be compromised. Even the most sophisticated and tech-savvy of us can be ensnared if we are tired, in a rush, or just unfamiliar with the latest methods hackers are using. Remember that hackers are working full-time on ways to fool us, so it’s very difficult to keep ahead of them.

The best defense is to make sure that any data that could compromised by hackers—basically all of the data that is reachable via your computer—is not your only copy. You do that by maintaining an active and reliable backup strategy.

Files that are backed up to cloud storage, such as with Backblaze, are not vulnerable to attacks on your local computer in the way that local files, attached drives, network drives, or sync services like Dropbox that have local directories on your computer are.

In the event that your computer is compromised and your files are lost or encrypted, you can recover your files if you have a cloud backup that is beyond the reach of attacks on your computer.

The post Top Ten Ways to Protect Yourself Against Phishing Attacks appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

How To Get Your First 1,000 Customers

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/how-to-get-your-first-1000-customers/

PR for getting your first 1000 customers

If you launch your startup and no one knows, did you actually launch? As mentioned in my last post, our initial launch target was to get a 1,000 people to use our service. But how do you get even 1,000 people to sign up for your service when no one knows who you are?

There are a variety of methods to attract your first 1,000 customers, but launching with the press is my favorite. I’ll explain why and how to do it below.

Paths to Attract Your First 1,000 Customers

Social following: If you have a massive social following, those people are a reasonable target for what you’re offering. In particular if your relationship with them is one where they would buy something you recommend, this can be one of the easiest ways to get your initial customers. However, building this type of following is non-trivial and often is done over several years.

Press not only provides awareness and customers, but credibility and SEO benefits as well

Paid advertising: The advantage of paid ads is you have control over when they are presented and what they say. The primary disadvantage is they tend to be expensive, especially before you have your positioning, messaging, and funnel nailed.

Viral: There are certainly examples of companies that launched with a hugely viral video, blog post, or promotion. While fantastic if it happens, even if you do everything right, the likelihood of massive virality is miniscule and the conversion rate is often low.

Press: As I said, this is my favorite. You don’t need to pay a PR agency and can go from nothing to launched in a couple weeks. Press not only provides awareness and customers, but credibility and SEO benefits as well.

How to Pitch the Press

It’s easy: Have a compelling story, find the right journalists, make their life easy, pitch and follow-up. Of course, each one of those has some nuance, so let’s dig in.

Have a compelling story

How to Get Attention When you’ve been working for months on your startup, it’s easy to get lost in the minutiae when talking to others. Stories that a journalist will write about need to be something their readers will care about. Knowing what story to tell and how to tell it is part science and part art. Here’s how you can get there:

The basics of your story

Ask yourself the following questions, and write down the answers:

  • What are we doing? What product service are we offering?
  • Why? What problem are we solving?
  • What is interesting or unique? Either about what we’re doing, how we’re doing it, or for who we’re doing it.

“But my story isn’t that exciting”

Neither was announcing a data backup company, believe me. Look for angles that make it compelling. Here are some:

  • Did someone on your team do something major before? (build a successful company/product, create some innovation, market something we all know, etc.)
  • Do you have an interesting investor or board member?
  • Is there a personal story that drove you to start this company?
  • Are you starting it in a unique place?
  • Did you come upon the idea in a unique way?
  • Can you share something people want to know that’s not usually shared?
  • Are you partnered with a well-known company?
  • …is there something interesting/entertaining/odd/shocking/touching/etc.?

It doesn’t get much less exciting than, “We’re launching a company that will backup your data.” But there were still a lot of compelling stories:

  • Founded by serial entrepreneurs, bootstrapped a capital-intensive company, committed to each other for a year without salary.
  • Challenging the way that every backup company before was set up by not asking customers to pick and choose files to backup.
  • Designing our own storage system.
  • Etc. etc.

For the initial launch, we focused on “unlimited for $5/month” and statistics from a survey we ran with Harris Interactive that said that 94% of people did not regularly backup their data.

It’s an old adage that “Everyone has a story.” Regardless of what you’re doing, there is always something interesting to share. Dig for that.

The headline

Once you’ve captured what you think the interesting story is, you’ve got to boil it down. Yes, you need the elevator pitch, but this is shorter…it’s the headline pitch. Write the headline that you would love to see a journalist write.

Regardless of what you’re doing, there is always something interesting to share. Dig for that.

Now comes the part where you have to be really honest with yourself: if you weren’t involved, would you care?

The “Techmeme Test”

One way I try to ground myself is what I call the “Techmeme Test”. Techmeme lists the top tech articles. Read the headlines. Imagine the headline you wrote in the middle of the page. If you weren’t involved, would you click on it? Is it more or less compelling than the others. Much of tech news is dominated by the largest companies. If you want to get written about, your story should be more compelling. If not, go back above and explore your story some more.

Embargoes, exclusives and calls-to-action

Journalists write about news. Thus, if you’ve already announced something and are then pitching a journalist to cover it, unless you’re giving her something significant that hasn’t been said, it’s no longer news. As a result, there are ‘embargoes’ and ‘exclusives’.

Embargoes

    • : An embargo simply means that you are sharing news with a journalist that they need to keep private until a certain date and time.

If you’re Apple, this may be a formal and legal document. In our case, it’s as simple as saying, “Please keep embargoed until 4/13/17 at 8am California time.” in the pitch. Some sites explicitly will not keep embargoes; for example The Information will only break news. If you want to launch something later, do not share information with journalists at these sites. If you are only working with a single journalist for a story, and your announcement time is flexible, you can jointly work out a date and time to announce. However, if you have a fixed launch time or are working with a few journalists, embargoes are key.

Exclusives: An exclusive means you’re giving something specifically to that journalist. Most journalists love an exclusive as it means readers have to come to them for the story. One option is to give a journalist an exclusive on the entire story. If it is your dream journalist, this may make sense. Another option, however, is to give exclusivity on certain pieces. For example, for your launch you could give an exclusive on funding detail & a VC interview to a more finance-focused journalist and insight into the tech & a CTO interview to a more tech-focused journalist.

Call-to-Action: With our launch we gave TechCrunch, Ars Technica, and SimplyHelp URLs that gave the first few hundred of their readers access to the private beta. Once those first few hundred users from each site downloaded, the beta would be turned off.

Thus, we used a combination of embargoes, exclusives, and a call-to-action during our initial launch to be able to brief journalists on the news before it went live, give them something they could announce as exclusive, and provide a time-sensitive call-to-action to the readers so that they would actually sign up and not just read and go away.

How to Find the Most Authoritative Sites / Authors

“If a press release is published and no one sees it, was it published?” Perhaps the time existed when sending a press release out over the wire meant journalists would read it and write about it. That time has long been forgotten. Over 1,000 unread press releases are published every day. If you want your compelling story to be covered, you need to find the handful of journalists that will care.

Determine the publications

Find the publications that cover the type of story you want to share. If you’re in tech, Techmeme has a leaderboard of publications ranked by leadership and presence. This list will tell you which publications are likely to have influence. Visit the sites and see if your type of story appears on their site. But, once you’ve determined the publication do NOT send a pitch their “[email protected]” or “[email protected]” email addresses. In all the times I’ve done that, I have never had a single response. Those email addresses are likely on every PR, press release, and spam list and unlikely to get read. Instead…

Determine the journalists

Once you’ve determined which publications cover your area, check which journalists are doing the writing. Skim the articles and search for keywords and competitor names.

Over 1,000 unread press releases are published every day.

Identify one primary journalist at the publication that you would love to have cover you, and secondary ones if there are a few good options. If you’re not sure which one should be the primary, consider a few tests:

  • Do they truly seem to care about the space?
  • Do they write interesting/compelling stories that ‘get it’?
  • Do they appear on the Techmeme leaderboard?
  • Do their articles get liked/tweeted/shared and commented on?
  • Do they have a significant social presence?

Leveraging Google

Google author search by date

In addition to Techmeme or if you aren’t in the tech space Google will become a must have tool for finding the right journalists to pitch. Below the search box you will find a number of tabs. Click on Tools and change the Any time setting to Custom range. I like to use the past six months to ensure I find authors that are actively writing about my market. I start with the All results. This will return a combination of product sites and articles depending upon your search term.

Scan for articles and click on the link to see if the article is on topic. If it is find the author’s name. Often if you click on the author name it will take you to a bio page that includes their Twitter, LinkedIn, and/or Facebook profile. Many times you will find their email address in the bio. You should collect all the information and add it to your outreach spreadsheet. Click here to get a copy. It’s always a good idea to comment on the article to start building awareness of your name. Another good idea is to Tweet or Like the article.

Next click on the News tab and set the same search parameters. You will get a different set of results. Repeat the same steps. Between the two searches you will have a list of authors that actively write for the websites that Google considers the most authoritative on your market.

How to find the most socially shared authors

Buzzsumo search for most shared by date

Your next step is to find the writers whose articles get shared the most socially. Go to Buzzsumo and click on the Most Shared tab. Enter search terms for your market as well as competitor names. Again I like to use the past 6 months as the time range. You will get a list of articles that have been shared the most across Facebook, LinkedIn, Twitter, Pinterest, and Google+. In addition to finding the most shared articles and their authors you can also see some of the Twitter users that shared the article. Many of those Twitter users are big influencers in your market so it’s smart to start following and interacting with them as well as the authors.

How to Find Author Email Addresses

Some journalists publish their contact info right on the stories. For those that don’t, a bit of googling will often get you the email. For example, TechCrunch wrote a story a few years ago where they published all of their email addresses, which was in response to this new service that charges a small fee to provide journalist email addresses. Sometimes visiting their twitter pages will link to a personal site, upon which they will share an email address.

Of course all is not lost if you don’t find an email in the bio. There are two good services for finding emails, https://app.voilanorbert.com/ and https://hunter.io/. For Voila Norbert enter the author name and the website you found their article on. The majority of the time you search for an author on a major publication Norbert will return an accurate email address. If it doesn’t try Hunter.io.

On Hunter.io enter the domain name and click on Personal Only. Then scroll through the results to find the author’s email. I’ve found Norbert to be more accurate overall but between the two you will find most major author’s email addresses.

Email, by the way, is not necessarily the best way to engage a journalist. Many are avid Twitter users. Follow them and engage – that means read/retweet/favorite their tweets; reply to their questions, and generally be helpful BEFORE you pitch them. Later when you email them, you won’t be just a random email address.

Don’t spam

Now that you have all these email addresses (possibly thousands if you purchased a list) – do NOT spam. It is incredibly tempting to think “I could try to figure out which of these folks would be interested, but if I just email all of them, I’ll save myself time and be more likely to get some of them to respond.” Don’t do it.

Follow them and engage – that means read/retweet/favorite their tweets; reply to their questions, and generally be helpful BEFORE you pitch them.

First, you’ll want to tailor your pitch to the individual. Second, it’s a small world and you’ll be known as someone who spams – reputation is golden. Also, don’t call journalists. Unless you know them or they’ve said they’re open to calls, you’re most likely to just annoy them.

Build a relationship

Build Trust with reporters Play the long game. You may be focusing just on the launch and hoping to get this one story covered, but if you don’t quickly flame-out, you will have many more opportunities to tell interesting stories that you’ll want the press to cover. Be honest and don’t exaggerate.
When you have 500 users it’s tempting to say, “We’ve got thousands!” Don’t. The good journalists will see through it and it’ll likely come back to bite you later. If you don’t know something, say “I don’t know but let me find out for you.” Most journalists want to write interesting stories that their readers will appreciate. Help them do that. Build deeper relationships with 5 – 10 journalists, rather than spamming thousands.

Stay organized

It doesn’t need to be complicated, but keep a spreadsheet that includes the name, publication, and contact info of the journalists you care about. Then, use it to keep track of who you’ve pitched, who’s responded, whether you’ve sent them the materials they need, and whether they intend to write/have written.

Make their life easy

Journalists have a million PR people emailing them, are actively engaging with readers on Twitter and in the comments, are tracking their metrics, are working their sources…and all the while needing to publish new articles. They’re busy. Make their life easy and they’re more likely to engage with yours.

Get to know them

Before sending them a pitch, know what they’ve written in the space. If you tell them how your story relates to ones they’ve written, it’ll help them put the story in context, and enable them to possibly link back to a story they wrote before.

Prepare your materials

Journalists will need somewhere to get more info (prepare a fact sheet), a URL to link to, and at least one image (ideally a few to choose from.) A fact sheet gives bite-sized snippets of information they may need about your startup or product: what it is, how big the market is, what’s the pricing, who’s on the team, etc. The URL is where their reader will get the product or more information from you. It doesn’t have to be live when you’re pitching, but you should be able to tell what the URL will be. The images are ones that they could embed in the article: a product screenshot, a CEO or team photo, an infographic. Scan the types of images included in their articles. Don’t send any of these in your pitch, but have them ready. Studies, stats, customer/partner/investor quotes are also good to have.

Pitch

A pitch has to be short and compelling.

Subject Line

Think back to the headline you want. Is it really compelling? Can you shorten it to a subject line? Include what’s happening and when. For Mike Arrington at Techcrunch, our first subject line was “Startup doing an ‘online time machine’”. Later I would include, “launching June 6th”.

For John Timmer at ArsTechnica, it was “Demographics data re: your 4/17 article”. Why? Because he wrote an article titled “WiFi popular with the young people; backups, not so much”. Since we had run a demographics survey on backups, I figured as a science editor he’d be interested in this additional data.

Body

A few key things about the body of the email. It should be short and to the point, no more than a few sentences. Here was my actual, original pitch email to John:

Hey John,

We’re launching Backblaze next week which provides a Time Machine-online type of service. As part of doing some research I read your article about backups not being popular with young people and that you had wished Accenture would have given you demographics. In prep for our invite-only launch I sponsored Harris Interactive to get demographic data on who’s doing backups and if all goes well, I should have that data on Friday.

Next week starts Backup Awareness Month (and yes, probably Clean Your House Month and Brush Your Teeth Month)…but nonetheless…good time to remind readers to backup with a bit of data?

Would you be interested in seeing/talking about the data when I get it?

Would you be interested in getting a sneak peak at Backblaze? (I could give you some invite codes for your readers as well.)

Gleb Budman        

CEO and Co-Founder

Backblaze, Inc.

Automatic, Secure, High-Performance Online Backup

Cell: XXX-XXX-XXXX

The Good: It said what we’re doing, why this relates to him and his readers, provides him information he had asked for in an article, ties to something timely, is clearly tailored for him, is pitched by the CEO and Co-Founder, and provides my cell.

The Bad: It’s too long.

I got better later. Here’s an example:

Subject: Does temperature affect hard drive life?

Hi Peter, there has been much debate about whether temperature affects how long a hard drive lasts. Following up on the Backblaze analyses of how long do drives last & which drives last the longest (that you wrote about) we’ve now analyzed the impact of heat on the nearly 40,000 hard drives we have and found that…

We’re going to publish the results this Monday, 5/12 at 5am California-time. Want a sneak peak of the analysis?

Timing

A common question is “When should I launch?” What day, what time? I prefer to launch on Tuesday at 8am California-time. Launching earlier in the week gives breathing room for the news to live longer. While your launch may be a single article posted and that’s that, if it ends up a larger success, earlier in the week allows other journalists (including ones who are in other countries) to build on the story. Monday announcements can be tough because the journalists generally need to have their stories finished by Friday, and while ideally everything is buttoned up beforehand, startups sometimes use the weekend as overflow before a launch.

The 8am California-time is because it allows articles to be published at the beginning of the day West Coast and around lunch-time East Coast. Later and you risk it being past publishing time for the day. We used to launch at 5am in order to be morning for the East Coast, but it did not seem to have a significant benefit in coverage or impact, but did mean that the entire internal team needed to be up at 3am or 4am. Sometimes that’s critical, but I prefer to not burn the team out when it’s not.

Finally, try to stay clear of holidays, major announcements and large conferences. If Apple is coming out with their next iPhone, many of the tech journalists will be busy at least a couple days prior and possibly a week after. Not always obvious, but if you can, find times that are otherwise going to be slow for news.

Follow-up

There is a fine line between persistence and annoyance. I once had a journalist write me after we had an announcement that was covered by the press, “Why didn’t you let me know?! I would have written about that!” I had sent him three emails about the upcoming announcement to which he never responded.

My general rule is 3 emails.

Ugh. However, my takeaway from this isn’t that I should send 10 emails to every journalist. It’s that sometimes these things happen.

My general rule is 3 emails. If I’ve identified a specific journalist that I think would be interested and have a pitch crafted for her, I’ll send her the email ideally 2 weeks prior to the announcement. I’ll follow-up a week later, and one more time 2 days prior. If she ever says, “I’m not interested in this topic,” I note it and don’t email her on that topic again.

If a journalist wrote, I read the article and engage in the comments (or someone on our team, such as our social guy, @YevP does). We’ll often promote the story through our social channels and email our employees who may choose to share the story as well. This helps us, but also helps the journalist get their story broader reach. Again, the goal is to build a relationship with the journalists your space. If there’s something relevant to your customers that the journalist wrote, you’re providing a service to your customers AND helping the journalist get the word out about the article.

At times the stories also end up shared on sites such as Hacker News, Reddit, Slashdot, or become active conversations on Twitter. Again, we try to engage there and respond to questions (when we do, we are always clear that we’re from Backblaze.)

And finally, I’ll often send a short thank you to the journalist.

Getting Your First 1,000 Customers With Press

As I mentioned at the beginning, there is more than one way to get your first 1,000 customers. My favorite is working with the press to share your story. If you figure out your compelling story, find the right journalists, make their life easy, pitch and follow-up, you stand a high likelyhood of getting coverage and customers. Better yet, that coverage will provide credibility for your company, and if done right, will establish you as a resource for the press for the future.

Like any muscle, this process takes working out. The first time may feel a bit daunting, but just take the steps one at a time. As you do this a few times, the process will be easier and you’ll know who to reach out and quickly determine what stories will be compelling.

The post How To Get Your First 1,000 Customers appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Protesters Physically Block HQ of Russian Web Blocking Watchdog

Post Syndicated from Andy original https://torrentfreak.com/protesters-physically-block-hq-of-russian-web-blocking-watchdog-170701/

Hardly a week goes by without the Russian web-blocking juggernaut rolling on to new targets. Whether they’re pirate websites, anonymity and proxy services, or sites that the government feels are inappropriate, web blocks are now a regular occurance in the region.

With thousands of domains and IP addresses blocked, the situation is serious. Just recently, however, blocks have been more problematic than usual. Telecoms watchdog Roskomnadzor, which oversees blocking, claims that innocent services are rarely hit. But critics say that overbroad IP address blockades are affecting the innocent.

Earlier this month there were reports that citizens across the country couldn’t access some of the country’s largest sites, including Google.ru, Yandex.ru, local Facebook variant vKontakte, and even the Telegram messaging app.

There have been various explanations for the problems, but the situation with Google appears to have stemmed from a redirect to an unauthorized gambling site. The problem was later resolved, and Google was removed from the register of banned sites, but critics say it should never have been included in the first place.

These and other developments have proven too much for some pro-freedom activists. This week they traveled to Roskomnadzor’s headquarters in St. Petersburg to give the blocking watchdog a small taste of its own medicine.

Activists from the “Open Russia” and “Civil Petersburg” movements positioned themselves outside the entrance to the telecom watchdog’s offices and built up their own barricade constructed from boxes. Each carried a label with the text “Blocked Citizens of Russia.”

Blockading the blockaders in Russia

“Freedom of information, like freedom of expression, are the basic values of our society. Those who try to attack them, must themselves be ‘blocked’ from society,” said Open Russia coordinator Andrei Pivovarov.

Rather like Internet blockades, the image above shows Open Russia’s blockade only partially doing its job by covering just three-quarters of Roskomnadzor’s entrance.

Whether that was deliberate or not is unknown but the video embedded below clearly shows staff walking around its perimeter. The protestors were probably just being considerate, but there are suggestions that staff might have been using VPNs or Tor.

Moving forward, new advice from Roskomnadzor to ISPs is that they should think beyond IP address and domain name blocking and consider using Deep Packet Inspection. This would help ensure blocks are carried out more accurately, the watchdog says.

There’s even a suggestion that rather than doing their own website filtering, Internet service providers could buy a “ready cleaned” Internet feed from an approved supplier instead. This would remove the need for additional filtering at their end, it’s argued, but it sounds like more problems waiting to happen.

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

[$] ProofMode: a camera app for verifiable photography

Post Syndicated from corbet original https://lwn.net/Articles/726142/rss

The default apps on a mobile platform like Android are familiar targets for
replacement, especially for developers concerned about security. But while
messaging and voice apps (which can be replaced by Signal and Ostel, for
instance) may be the best known examples, the non-profit Guardian Project has taken up the
cause of improving the security features of the camera app. Its latest
such project is ProofMode, an app
to let users take photos and videos that can be verified as authentic by
third parties.

Building Loosely Coupled, Scalable, C# Applications with Amazon SQS and Amazon SNS

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/building-loosely-coupled-scalable-c-applications-with-amazon-sqs-and-amazon-sns/

 
Stephen Liedig, Solutions Architect

 

One of the many challenges professional software architects and developers face is how to make cloud-native applications scalable, fault-tolerant, and highly available.

Fundamental to your project success is understanding the importance of making systems highly cohesive and loosely coupled. That means considering the multi-dimensional facets of system coupling to support the distributed nature of the applications that you are building for the cloud.

By that, I mean addressing not only the application-level coupling (managing incoming and outgoing dependencies), but also considering the impacts of of platform, spatial, and temporal coupling of your systems. Platform coupling relates to the interoperability, or lack thereof, of heterogeneous systems components. Spatial coupling deals with managing components at a network topology level or protocol level. Temporal, or runtime coupling, refers to the ability of a component within your system to do any kind of meaningful work while it is performing a synchronous, blocking operation.

The AWS messaging services, Amazon SQS and Amazon SNS, help you deal with these forms of coupling by providing mechanisms for:

  • Reliable, durable, and fault-tolerant delivery of messages between application components
  • Logical decomposition of systems and increased autonomy of components
  • Creating unidirectional, non-blocking operations, temporarily decoupling system components at runtime
  • Decreasing the dependencies that components have on each other through standard communication and network channels

Following on the recent topic, Building Scalable Applications and Microservices: Adding Messaging to Your Toolbox, in this post, I look at some of the ways you can introduce SQS and SNS into your architectures to decouple your components, and show how you can implement them using C#.

Walkthrough

To illustrate some of these concepts, consider a web application that processes customer orders. As good architects and developers, you have followed best practices and made your application scalable and highly available. Your solution included implementing load balancing, dynamic scaling across multiple Availability Zones, and persisting orders in a Multi-AZ Amazon RDS database instance, as in the following diagram.


In this example, the application is responsible for handling and persisting the order data, as well as dealing with increases in traffic for popular items.

One potential point of vulnerability in the order processing workflow is in saving the order in the database. The business expects that every order has been persisted into the database. However, any potential deadlock, race condition, or network issue could cause the persistence of the order to fail. Then, the order is lost with no recourse to restore the order.

With good logging capability, you may be able to identify when an error occurred and which customer’s order failed. This wouldn’t allow you to “restore” the transaction, and by that stage, your customer is no longer your customer.

As illustrated in the following diagram, introducing an SQS queue helps improve your ordering application. Using the queue isolates the processing logic into its own component and runs it in a separate process from the web application. This, in turn, allows the system to be more resilient to spikes in traffic, while allowing work to be performed only as fast as necessary in order to manage costs.


In addition, you now have a mechanism for persisting orders as messages (with the queue acting as a temporary database), and have moved the scope of your transaction with your database further down the stack. In the event of an application exception or transaction failure, this ensures that the order processing can be retired or redirected to the Amazon SQS Dead Letter Queue (DLQ), for re-processing at a later stage. (See the recent post, Using Amazon SQS Dead-Letter Queues to Control Message Failure, for more information on dead-letter queues.)

Scaling the order processing nodes

This change allows you now to scale the web application frontend independently from the processing nodes. The frontend application can continue to scale based on metrics such as CPU usage, or the number of requests hitting the load balancer. Processing nodes can scale based on the number of orders in the queue. Here is an example of scale-in and scale-out alarms that you would associate with the scaling policy.

Scale-out Alarm

aws cloudwatch put-metric-alarm --alarm-name AddCapacityToCustomerOrderQueue --metric-name ApproximateNumberOfMessagesVisible --namespace "AWS/SQS" 
--statistic Average --period 300 --threshold 3 --comparison-operator GreaterThanOrEqualToThreshold --dimensions Name=QueueName,Value=customer-orders
--evaluation-periods 2 --alarm-actions <arn of the scale-out autoscaling policy>

Scale-in Alarm

aws cloudwatch put-metric-alarm --alarm-name RemoveCapacityFromCustomerOrderQueue --metric-name ApproximateNumberOfMessagesVisible --namespace "AWS/SQS" 
 --statistic Average --period 300 --threshold 1 --comparison-operator LessThanOrEqualToThreshold --dimensions Name=QueueName,Value=customer-orders
 --evaluation-periods 2 --alarm-actions <arn of the scale-in autoscaling policy>

In the above example, use the ApproximateNumberOfMessagesVisible metric to discover the queue length and drive the scaling policy of the Auto Scaling group. Another useful metric is ApproximateAgeOfOldestMessage, when applications have time-sensitive messages and developers need to ensure that messages are processed within a specific time period.

Scaling the order processing implementation

On top of scaling at an infrastructure level using Auto Scaling, make sure to take advantage of the processing power of your Amazon EC2 instances by using as many of the available threads as possible. There are several ways to implement this. In this post, we build a Windows service that uses the BackgroundWorker class to process the messages from the queue.

Here’s a closer look at the implementation. In the first section of the consuming application, use a loop to continually poll the queue for new messages, and construct a ReceiveMessageRequest variable.

public static void PollQueue()
{
    while (_running)
    {
        Task<ReceiveMessageResponse> receiveMessageResponse;

        // Pull messages off the queue
        using (var sqs = new AmazonSQSClient())
        {
            const int maxMessages = 10;  // 1-10

            //Receiving a message
            var receiveMessageRequest = new ReceiveMessageRequest
            {
                // Get URL from Configuration
                QueueUrl = _queueUrl, 
                // The maximum number of messages to return. 
                // Fewer messages might be returned. 
                MaxNumberOfMessages = maxMessages, 
                // A list of attributes that need to be returned with message.
                AttributeNames = new List<string> { "All" },
                // Enable long polling. 
                // Time to wait for message to arrive on queue.
                WaitTimeSeconds = 5 
            };

            receiveMessageResponse = sqs.ReceiveMessageAsync(receiveMessageRequest);
        }

The WaitTimeSeconds property of the ReceiveMessageRequest specifies the duration (in seconds) that the call waits for a message to arrive in the queue before returning a response to the calling application. There are a few benefits to using long polling:

  • It reduces the number of empty responses by allowing SQS to wait until a message is available in the queue before sending a response.
  • It eliminates false empty responses by querying all (rather than a limited number) of the servers.
  • It returns messages as soon any message becomes available.

For more information, see Amazon SQS Long Polling.

After you have returned messages from the queue, you can start to process them by looping through each message in the response and invoking a new BackgroundWorker thread.

// Process messages
if (receiveMessageResponse.Result.Messages != null)
{
    foreach (var message in receiveMessageResponse.Result.Messages)
    {
        Console.WriteLine("Received SQS message, starting worker thread");

        // Create background worker to process message
        BackgroundWorker worker = new BackgroundWorker();
        worker.DoWork += (obj, e) => ProcessMessage(message);
        worker.RunWorkerAsync();
    }
}
else
{
    Console.WriteLine("No messages on queue");
}

The event handler, ProcessMessage, is where you implement business logic for processing orders. It is important to have a good understanding of how long a typical transaction takes so you can set a message VisibilityTimeout that is long enough to complete your operation. If order processing takes longer than the specified timeout period, the message becomes visible on the queue. Other nodes may pick it and process the same order twice, leading to unintended consequences.

Handling Duplicate Messages

In order to manage duplicate messages, seek to make your processing application idempotent. In mathematics, idempotent describes a function that produces the same result if it is applied to itself:

f(x) = f(f(x))

No matter how many times you process the same message, the end result is the same (definition from Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, Hohpe and Wolf, 2004).

There are several strategies you could apply to achieve this:

  • Create messages that have inherent idempotent characteristics. That is, they are non-transactional in nature and are unique at a specified point in time. Rather than saying “place new order for Customer A,” which adds a duplicate order to the customer, use “place order <orderid> on <timestamp> for Customer A,” which creates a single order no matter how often it is persisted.
  • Deliver your messages via an Amazon SQS FIFO queue, which provides the benefits of message sequencing, but also mechanisms for content-based deduplication. You can deduplicate using the MessageDeduplicationId property on the SendMessage request or by enabling content-based deduplication on the queue, which generates a hash for MessageDeduplicationId, based on the content of the message, not the attributes.
var sendMessageRequest = new SendMessageRequest
{
    QueueUrl = _queueUrl,
    MessageBody = JsonConvert.SerializeObject(order),
    MessageGroupId = Guid.NewGuid().ToString("N"),
    MessageDeduplicationId = Guid.NewGuid().ToString("N")
};
  • If using SQS FIFO queues is not an option, keep a message log of all messages attributes processed for a specified period of time, as an alternative to message deduplication on the receiving end. Verifying the existence of the message in the log before processing the message adds additional computational overhead to your processing. This can be minimized through low latency persistence solutions such as Amazon DynamoDB. Bear in mind that this solution is dependent on the successful, distributed transaction of the message and the message log.

Handling exceptions

Because of the distributed nature of SQS queues, it does not automatically delete the message. Therefore, you must explicitly delete the message from the queue after processing it, using the message ReceiptHandle property (see the following code example).

However, if at any stage you have an exception, avoid handling it as you normally would. The intention is to make sure that the message ends back on the queue, so that you can gracefully deal with intermittent failures. Instead, log the exception to capture diagnostic information, and swallow it.

By not explicitly deleting the message from the queue, you can take advantage of the VisibilityTimeout behavior described earlier. Gracefully handle the message processing failure and make the unprocessed message available to other nodes to process.

In the event that subsequent retries fail, SQS automatically moves the message to the configured DLQ after the configured number of receives has been reached. You can further investigate why the order process failed. Most importantly, the order has not been lost, and your customer is still your customer.

private static void ProcessMessage(Message message)
{
    using (var sqs = new AmazonSQSClient())
    {
        try
        {
            Console.WriteLine("Processing message id: {0}", message.MessageId);

            // Implement messaging processing here
            // Ensure no downstream resource contention (parallel processing)
            // <your order processing logic in here…>
            Console.WriteLine("{0} Thread {1}: {2}", DateTime.Now.ToString("s"), Thread.CurrentThread.ManagedThreadId, message.MessageId);
            
            // Delete the message off the queue. 
            // Receipt handle is the identifier you must provide 
            // when deleting the message.
            var deleteRequest = new DeleteMessageRequest(_queueName, message.ReceiptHandle);
            sqs.DeleteMessageAsync(deleteRequest);
            Console.WriteLine("Processed message id: {0}", message.MessageId);

        }
        catch (Exception ex)
        {
            // Do nothing.
            // Swallow exception, message will return to the queue when 
            // visibility timeout has been exceeded.
            Console.WriteLine("Could not process message due to error. Exception: {0}", ex.Message);
        }
    }
}

Using SQS to adapt to changing business requirements

One of the benefits of introducing a message queue is that you can accommodate new business requirements without dramatically affecting your application.

If, for example, the business decided that all orders placed over $5000 are to be handled as a priority, you could introduce a new “priority order” queue. The way the orders are processed does not change. The only significant change to the processing application is to ensure that messages from the “priority order” queue are processed before the “standard order” queue.

The following diagram shows how this logic could be isolated in an “order dispatcher,” whose only purpose is to route order messages to the appropriate queue based on whether the order exceeds $5000. Nothing on the web application or the processing nodes changes other than the target queue to which the order is sent. The rates at which orders are processed can be achieved by modifying the poll rates and scalability settings that I have already discussed.

Extending the design pattern with Amazon SNS

Amazon SNS supports reliable publish-subscribe (pub-sub) scenarios and push notifications to known endpoints across a wide variety of protocols. It eliminates the need to periodically check or poll for new information and updates. SNS supports:

  • Reliable storage of messages for immediate or delayed processing
  • Publish / subscribe – direct, broadcast, targeted “push” messaging
  • Multiple subscriber protocols
  • Amazon SQS, HTTP, HTTPS, email, SMS, mobile push, AWS Lambda

With these capabilities, you can provide parallel asynchronous processing of orders in the system and extend it to support any number of different business use cases without affecting the production environment. This is commonly referred to as a “fanout” scenario.

Rather than your web application pushing orders to a queue for processing, send a notification via SNS. The SNS messages are sent to a topic and then replicated and pushed to multiple SQS queues and Lambda functions for processing.

As the diagram above shows, you have the development team consuming “live” data as they work on the next version of the processing application, or potentially using the messages to troubleshoot issues in production.

Marketing is consuming all order information, via a Lambda function that has subscribed to the SNS topic, inserting the records into an Amazon Redshift warehouse for analysis.

All of this, of course, is happening without affecting your order processing application.

Summary

While I haven’t dived deep into the specifics of each service, I have discussed how these services can be applied at an architectural level to build loosely coupled systems that facilitate multiple business use cases. I’ve also shown you how to use infrastructure and application-level scaling techniques, so you can get the most out of your EC2 instances.

One of the many benefits of using these managed services is how quickly and easily you can implement powerful messaging capabilities in your systems, and lower the capital and operational costs of managing your own messaging middleware.

Using Amazon SQS and Amazon SNS together can provide you with a powerful mechanism for decoupling application components. This should be part of design considerations as you architect for the cloud.

For more information, see the Amazon SQS Developer Guide and Amazon SNS Developer Guide. You’ll find tutorials on all the concepts covered in this post, and more. To can get started using the AWS console or SDK of your choice visit:

Happy messaging!

Using Amazon SQS Dead-Letter Queues to Control Message Failure

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/using-amazon-sqs-dead-letter-queues-to-control-message-failure/


Michael G. Khmelnitsky, Senior Programmer Writer

 

Sometimes, messages can’t be processed because of a variety of possible issues, such as erroneous conditions within the producer or consumer application. For example, if a user places an order within a certain number of minutes of creating an account, the producer might pass a message with an empty string instead of a customer identifier. Occasionally, producers and consumers might fail to interpret aspects of the protocol that they use to communicate, causing message corruption or loss. Also, the consumer’s hardware errors might corrupt message payload. For these reasons, messages that can’t be processed in a timely manner are delivered to a dead-letter queue.

The recent post Building Scalable Applications and Microservices: Adding Messaging to Your Toolbox gives an overview of messaging in the microservice architecture of modern applications. This post explains how and when you should use dead-letter queues to gain better control over message handling in your applications. It also offers some resources for configuring a dead-letter queue in Amazon Simple Queue Service (SQS).

What are the benefits of dead-letter queues?

The main task of a dead-letter queue is handling message failure. A dead-letter queue lets you set aside and isolate messages that can’t be processed correctly to determine why their processing didn’t succeed. Setting up a dead-letter queue allows you to do the following:

  • Configure an alarm for any messages delivered to a dead-letter queue.
  • Examine logs for exceptions that might have caused messages to be delivered to a dead-letter queue.
  • Analyze the contents of messages delivered to a dead-letter queue to diagnose software or the producer’s or consumer’s hardware issues.
  • Determine whether you have given your consumer sufficient time to process messages.

How do high-throughput, unordered queues handle message failure?

High-throughput, unordered queues (sometimes called standard or storage queues) keep processing messages until the expiration of the retention period. This helps ensure continuous processing of messages, which minimizes the chances of your queue being blocked by messages that can’t be processed. It also ensures fast recovery for your queue.

In a system that processes thousands of messages, having a large number of messages that the consumer repeatedly fails to acknowledge and delete might increase costs and place extra load on the hardware. Instead of trying to process failing messages until they expire, it is better to move them to a dead-letter queue after a few processing attempts.

Note: This queue type often allows a high number of in-flight messages. If the majority of your messages can’t be consumed and aren’t sent to a dead-letter queue, your rate of processing valid messages can slow down. Thus, to maintain the efficiency of your queue, you must ensure that your application handles message processing correctly.

How do FIFO queues handle message failure?

FIFO (first-in-first-out) queues (sometimes called service bus queues) help ensure exactly-once processing by consuming messages in sequence from a message group. Thus, although the consumer can continue to retrieve ordered messages from another message group, the first message group remains unavailable until the message blocking the queue is processed successfully.

Note: This queue type often allows a lower number of in-flight messages. Thus, to help ensure that your FIFO queue doesn’t get blocked by a message, you must ensure that your application handles message processing correctly.

When should I use a dead-letter queue?

  • Do use dead-letter queues with high-throughput, unordered queues. You should always take advantage of dead-letter queues when your applications don’t depend on ordering. Dead-letter queues can help you troubleshoot incorrect message transmission operations. Note: Even when you use dead-letter queues, you should continue to monitor your queues and retry sending messages that fail for transient reasons.
  • Do use dead-letter queues to decrease the number of messages and to reduce the possibility of exposing your system to poison-pill messages (messages that can be received but can’t be processed).
  • Don’t use a dead-letter queue with high-throughput, unordered queues when you want to be able to keep retrying the transmission of a message indefinitely. For example, don’t use a dead-letter queue if your program must wait for a dependent process to become active or available.
  • Don’t use a dead-letter queue with a FIFO queue if you don’t want to break the exact order of messages or operations. For example, don’t use a dead-letter queue with instructions in an Edit Decision List (EDL) for a video editing suite, where changing the order of edits changes the context of subsequent edits.

How do I get started with dead-letter queues in Amazon SQS?

Amazon SQS is a fully managed service that offers reliable, highly scalable hosted queues for exchanging messages between applications or microservices. Amazon SQS moves data between distributed application components and helps you decouple these components. It supports both standard queues and FIFO queues. To configure a queue as a dead-letter queue, you can use the AWS Management Console or the Amazon SQS SetQueueAttributes API action.

To get started with dead-letter queues in Amazon SQS, see the following topics in the Amazon SQS Developer Guide:

To start working with dead-letter queues programmatically, see the following resources:

AWS Greengrass – Run AWS Lambda Functions on Connected Devices

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-greengrass-run-aws-lambda-functions-on-connected-devices/

I first told you about AWS Greengrass in the post that I published during re:Invent (AWS Greengrass – Ubiquitous Real-World Computing). We launched a limited preview of Greengrass at that time and invited you to sign up if you were interested.

As I noted at the time, many AWS customers want to collect and process data out in the field, where connectivity is often slow and sometimes either intermittent or unreliable. Greengrass allows them to extend the AWS programming model to small, simple, field-based devices. It builds on AWS IoT and AWS Lambda, and supports access to the ever-increasing variety of services that are available in the AWS Cloud.

Greengrass gives you access to compute, messaging, data caching, and syncing services that run in the field, and that do not depend on constant, high-bandwidth connectivity to an AWS Region. You can write Lambda functions in Python 2.7 and deploy them to your Greengrass devices from the cloud while using device shadows to maintain state. Your devices and peripherals can talk to each other using local messaging that does not pass through the cloud.

Now Generally Available
Today we are making Greengrass generally available in the US East (Northern Virginia) and US West (Oregon) Regions. During the preview, AWS customers were able to get hands-on experience with Greengrass and to start building applications and businesses around it. I’ll share a few of these early successes later in this post.

The Greengrass Core code runs on each device. It allows you to deploy and run Lambda applications on the device, supports local MQTT messaging across a secure network, and also ensures that conversations between devices and the cloud are made across secure connections. The Greengrass Core also supports secure, over-the-air software updates, including Lambda functions. It includes a message broker, a Lambda runtime, a Thing Shadows implementation, and a deployment agent. Greengrass Core and (optionally) other devices make up a Greengrass Group. The group includes configuration data, the list of devices and the identity of the Greengrass Core, a list of Lambda functions, and a set of subscriptions that define where the messages should go. All of this information is copied to the Greengrass core devices during the deployment process.

Your Lambda functions can use APIs in three distinct SDKs:

AWS SDK for Python – This SDK allows your code to interact with Amazon Simple Storage Service (S3), Amazon DynamoDB, Amazon Simple Queue Service (SQS), and other AWS services.

AWS IoT Device SDK – This SDK (available for Node.js, Python, Java, and C++) helps you to connect your hardware devices to AWS IoT. The C++ SDK has a few extra features including access to the Greengrass Discovery Service and support for root CA downloads.

AWS Greengrass Core SDK – This SDK provides APIs that allow local invocation of other Lambda functions, publish messages, and work with thing shadows.

You can run the Greengrass Core on x86 and ARM devices that have version 4.4.11 (or newer) of the Linux kernel, with the OverlayFS and user namespace features enabled. While most deployments of Greengrass will be targeted at specialized, industrial-grade hardware, you can also run the Greengrass Core on a Raspberry Pi or an EC2 instance for development and test purposes.

For this post, I used a Raspberry Pi attached to a BrickPi, connected to my home network via WiFi:

The Raspberry Pi, the BrickPi, the case, and all of the other parts are available in the BrickPi 3 Starter Kit. You will need some Linux command-line expertise and a decent amount of manual dexterity to put all of this together, but if I did it then you surely can.

Greengrass in Action
I can access Greengrass from the Console, API, or CLI. I’ll use the Console. The intro page of the Greengrass Console lets me define groups, add Greengrass Cores, and add devices to my groups:

I click on Get Started and then on Use easy creation:

Then I name my group:

And name my first Greengrass Core:

I’m ready to go, so I click on Create Group and Core:

This runs for a few seconds and then offers up my security resources (two keys and a certificate) for downloading, along with the Greengrass Core:

I download the security resources and put them in a safe place, and select and download the desired version of the Greengrass Core software (ARMv7l for my Raspberry Pi), and click on Finish.

Now I power up my Pi, and copy the security resources and the software to it (I put them in an S3 bucket and pulled them down with wget). Here’s my shell history at that point:

Following the directions in the user guide, I create a new user and group, run the rpi-update script, and install several packages including sqlite3 and openssl. After a couple of reboots, I am ready to proceed!

Next, still following the directions, I untar the Greengrass Core software and move the security resources to their final destination (/greengrass/configuration/certs), giving them generic names along the way. Here’s what the directory looks like:

The next step is to associate the core with an AWS IoT thing. I return to the Console, click through the group and the Greengrass Core, and find the Thing ARN:

I insert the names of the certificates and the Thing ARN into the config.json file, and also fill in the missing sections of the iotHost and ggHost:

I start the Greengrass demon (this was my second attempt; I had a typo in one of my path names the first time around):

After all of this pleasant time at the command line (taking me back to my Unix v7 and BSD 4.2 days), it is time to go visual once again! I visit my AWS IoT dashboard and see that my Greengrass Core is making connections to IoT:

I go to the Lambda Console and create a Lambda function using the Python 2.7 runtime (the IAM role does not matter here):

I publish the function in the usual way and, hop over to the Greengrass Console, click on my group, and choose to add a Lambda function:

Then I choose the version to deploy:

I also configure the function to be long-lived instead of on-demand:

My code will publish messages to AWS IoT, so I create a subscription by specifying the source and destination:

I set up a topic filter (hello/world) on the subscription as well:

I confirm my settings and save my subscription and I am just about ready to deploy my code. I revisit my group, click on Deployments, and choose Deploy from the Actions menu:

I choose Automatic detection to move forward:

Since this is my first deployment, I need to create a service-level role that gives Greengrass permission to access other AWS services. I simply click on Grant permission:

I can see the status of each deployment:

The code is now running on my Pi! It publishes messages to topic hello/world; I can see them by going to the IoT Console, clicking on Test, and subscribing to the topic:

And here are the messages:

With all of the setup work taken care of, I can do iterative development by uploading, publishing, and deploying new versions of my code. I plan to use the BrickPi to control some LEGO Technic motors and to publish data collected from some sensors. Stay tuned for that post!

Greengrass Pricing
You can run the Greengrass Core on three devices free for one year as part of the AWS Free Tier. At the next level (3 to 10,000 devices) two options are available:

  • Pay as You Go – $0.16 per month per device.
  • Annual Commitment – $1.49 per year per device, a 17.5% savings.

If you want to run the Greengrass Core on more than 10,000 devices or make a longer commitment, please get in touch with us; details on all pricing models are on the Greengrass Pricing page.

Jeff;

The US Senate Is Using Signal

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/05/the_us_senate_i.html

The US Senate just approved Signal for staff use. Signal is a secure messaging app with no backdoor, and no large corporate owner who can be pressured to install a backdoor.

Susan Landau comments.

Maybe I’m being optimistic, but I think we just won the Crypto War. A very important part of the US government is prioritizing security over surveillance.

AWS Online Tech Talks – May 2017

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-2017/

Spring has officially sprung. As you enjoy the blossoming of May flowers, it may be worthy to also note some of the great tech talks blossoming online during the month of May. This month’s AWS Online Tech Talks features sessions on topics like AI, DevOps, Data, and Serverless just to name a few.

May 2017 – Schedule

Below is the upcoming schedule for the live, online technical sessions scheduled for the month of May. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts. All schedule times for the online tech talks are shown in the Pacific Time (PDT) time zone.

Webinars featured this month are:

Monday, May 15

Artificial Intelligence

9:00 AM – 10:00 AM: Integrate Your Amazon Lex Chatbot with Any Messaging Service

 

Tuesday, May 16

Compute

10:30 AM – 11:30 AM: Deep Dive on Amazon EC2 F1 Instance

IoT

12:00 Noon – 1:00 PM: How to Connect Your Own Creations with AWS IoT

Wednesday, May 17

Management Tools

9:00 AM – 10:00 AM: OpsWorks for Chef Automate – Automation Made Easy!

Serverless

10:30 AM – 11:30 AM: Serverless Orchestration with AWS Step Functions

Enterprise & Hybrid

12:00 Noon – 1:00 PM: Moving to the AWS Cloud: An Overview of the AWS Cloud Adoption Framework

 

Thursday, May 18

Compute

9:00 AM – 10:00 AM: Scaling Up Tenfold with Amazon EC2 Spot Instances

Big Data

10:30 AM – 11:30 AM: Building Analytics Pipelines for Games on AWS

12:00 Noon – 1:00 PM: Serverless Big Data Analytics using Amazon Athena and Amazon QuickSight

 

Monday, May 22

Artificial Intelligence

9:00 AM – 10:00 AM: What’s New with Amazon Rekognition

Serverless

10:30 AM – 11:30 AM: Building Serverless Web Applications

 

Tuesday, May 23

Hands-On Lab

8:30 – 10:00 AM: Hands On Lab: Windows Workloads on AWS

Big Data

10:30 AM – 11:30 AM: Streaming ETL for Data Lakes using Amazon Kinesis Firehose

DevOps

12:00 Noon – 1:00 PM: Deep Dive: Continuous Delivery for AI Applications with ECS

 

Wednesday, May 24

Storage

9:00 – 10:00 AM: Moving Data into the Cloud with AWS Transfer Services

Containers

12:00 Noon – 1:00 PM: Building a CICD Pipeline for Container Deployment to Amazon ECS

 

Thursday, May 25

Mobile

9:00 – 10:00 AM: Test Your Android App with Espresso and AWS Device Farm

Security & Identity

10:30 AM – 11:30 AM: Advanced Techniques for Federation of the AWS Management Console and Command Line Interface (CLI)

 

Tuesday, May 30

Databases

9:00 – 10:00 AM: DynamoDB: Architectural Patterns and Best Practices for Infinitely Scalable Applications

Compute

10:30 AM – 11:30 AM: Deep Dive on Amazon EC2 Elastic GPUs

Security & Identity

12:00 Noon – 1:00 PM: Securing Your AWS Infrastructure with Edge Services

 

Wednesday, May 31

Hands-On Lab

8:30 – 10:00 AM: Hands On Lab: Introduction to Microsoft SQL Server in AWS

Enterprise & Hybrid

10:30 AM – 11:30 AM: Best Practices in Planning a Large-Scale Migration to AWS

Databases

12:00 Noon – 1:00 PM: Convert and Migrate Your NoSQL Database or Data Warehouse to AWS

 

The AWS Online Tech Talks series covers a broad range of topics at varying technical levels. These sessions feature live demonstrations & customer examples led by AWS engineers and Solution Architects. Check out the AWS YouTube channel for more on-demand webinars on AWS technologies.

Tara

Amazon Simple Queue Service Introduces Server-Side Encryption for Queues

Post Syndicated from Craig Liebendorfer original https://aws.amazon.com/blogs/security/amazon-simple-queue-service-introduces-server-side-encryption-for-queues/

SQS + SSE image

You can now use Amazon Simple Queue Service (SQS) to exchange sensitive data between applications using server-side encryption (SSE). SQS is a fully managed message queuing service for reliably communicating between distributed software components and microservices at any scale. You can use SQS to take advantage of the scale, cost, and operational benefits of a managed messaging service. The addition of SSE allows you to transmit sensitive data with the increased security of using encrypted queues.

SQS SSE uses the 256-bit Advanced Encryption Standard (AES-256 GCM algorithm) to encrypt each message body by using a unique key. AWS Key Management Service (KMS) provides encryption key management. In addition, KMS works with AWS CloudTrail to provide logs of all encryption key usage to help meet your regulatory and compliance needs.

SQS SSE is now available in the US West (Oregon) and US East (Ohio) Regions, with more AWS Regions to follow. There is no additional charge for using encrypted queues, but there is a charge to use KMS. For more information about KMS and CloudTrail pricing, see AWS Key Management Service Pricing.

To learn more, see the full post on the AWS Blog.

– Craig

AWS X-Ray Update – General Availability, Including Lambda Integration

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-x-ray-update-general-availability-including-lambda-integration/

I first told you about AWS X-Ray at AWS re:Invent in my post, AWS X-Ray – See Inside Your Distributed Application. X-Ray allows you to trace requests made to your application as execution traverses Amazon EC2 instances, Amazon ECS containers, microservices, AWS database services, and AWS messaging services. It is designed for development and production use, and can handle simple three-tier applications as well as applications composed of thousands of microservices. As I showed you last year, X-Ray helps you to perform end-to-end tracing of requests, record a representative sample of the traces, see a map of the services and the trace data, and to analyze performance issues and errors. This helps you understand how your application and its underlying services are performing so you can identify and address the root cause of issues.

You can take a look at the full X-Ray walk-through in my earlier post to learn more.

We launched X-Ray in preview form at re:Invent and invited interested developers and architects to start using it. Today we are making the service generally available, with support in the US East (Northern Virginia), US West (Northern California), US East (Ohio), US West (Oregon), EU (Ireland), EU (Frankfurt), South America (São Paulo), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Sydney), and Asia Pacific (Mumbai) Regions.

New Lambda Integration (Preview)
During the preview period we fine-tuned the service and added AWS Lambda integration, which we are launching today in preview form. Now, Lambda developers can use X-Ray to gain visibility into their function executions and performance. Previously, Lambda customers who wanted to understand their application’s latency breakdown, diagnose slowdowns, or troubleshoot timeouts had to rely on custom logging and analysis.

In order to make use of this new integration, you simply ensure that the functions of interest have execution roles that gives the functions permission to write to X-Ray, and then enable tracing on a function-by-function basis (when you create new functions using the console, the proper permissions are assigned automatically). Then you use the X-Ray service map to see how your requests flow through your Lambda functions, EC2 instances, ECS containers, and so forth. You can identify the services and resources of interest, zoom in, examine detailed timing information, and then remedy the issue.

Each call to a Lambda function generates two or more nodes in the X-Ray map:

Lambda Service – This node represents the time spent within Lambda itself.

User Function – This node represents the execution time of the Lambda function.

Downstream Service Calls – These nodes represent any calls that the Lambda function makes to other services.

To learn more, read Using X-Ray with Lambda.

Now Available
We will begin to charge for the usage of X-Ray on May 1, 2017.

Pricing is based on the number of traces that you record, and the number that you analyze (each trace represent a request made to your application). You can record 100,000 traces and retrieve or scan 1,000,000 traces every month at no charge. Beyond that, you pay $5 for every million traces that you record and $0.50 for every million traces that you retrieve for analysis, with more info available on the AWS X-Ray Pricing page. You can visit the AWS Billing Console to see how many traces you have recorded or accessed (data collection began on March 1, 2017).

Check out AWS X-Ray and the new Lambda integration today and let me know what you think!

Jeff;

 

President Trump Signs Internet Privacy Repeal Into Law

Post Syndicated from Andy original https://torrentfreak.com/president-trump-signs-internet-privacy-repeal-into-law-170404/

In a major setback to those who value their online privacy in the United States, last week the House of Representatives voted to grant Internet service providers permission to sell subscribers’ browsing histories to third parties.

The bill repeals broadband privacy rules adopted last year by the Federal Communications Commission, which required ISPs to obtain subscribers’ consent before using their browsing records for advertising or marketing purposes.

Soon after, the Trump Administration officially announced its support for the bill, noting that the President’s advisors would advise him to sign it, should it be presented. Yesterday, that’s exactly what happened.

To howls of disapproval from Internet users and privacy advocates alike, President Trump signed into law a resolution that seriously undermines the privacy of all citizens using ISPs to get online in the US. The bill removes protections that were approved by the FCC in the final days of the Obama administration but had not yet gone into effect.

The dawning reality is that telecoms giants including Comcast, AT&T, and Verizon, are now free to collect and leverage the browsing histories of subscribers – no matter how sensitive – in order to better target them with advertising and other marketing.

The White House says that the changes will simply create an “equal playing field” between ISPs and Internet platforms such as Google and Facebook, who are already able to collect data for advertising purposes.

The repeal has drawn criticism from all sides, with Mozilla’s Executive Director Mark Surman openly urging the public to fight back.

“The repeal should be a call to action. And not just to badger our lawmakers,” Surman said.

“It should be an impetus to take online privacy into our own hands.”

With the bill now signed into law, that’s the only real solution if people want to claw back their privacy. Surman has a few suggestions, including the use of Tor and encrypted messaging apps like Signal. But like so many others recently, he leads with the use of VPN technology.

As reported last week, Google searches for the term VPN reached unprecedented levels when the public realized that their data would soon be up for grabs.

That trend continued through the weekend, with many major VPN providers reporting increased interest in their products.

Only time will tell if interest from the mainstream will continue at similar levels. However, in broad terms, the recent public outcry over privacy is only likely to accelerate the uptake of security products and the use of encryption as a whole. It could even prove to be the wake-up call the Internet needed.

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

AWS Hot Startups – March 2017

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-hot-startups-march-2017/

As the madness of March rounds up, take a break from all the basketball and check out the cool startups Tina Barr brings you for this month!

-Ana


The arrival of spring brings five new startups this month:

  • Amino Apps – providing social networks for hundreds of thousands of communities.
  • Appboy – empowering brands to strengthen customer relationships.
  • Arterys – revolutionizing the medical imaging industry.
  • Protenus – protecting patient data for healthcare organizations.
  • Syapse – improving targeted cancer care with shared data from across the country.

In case you missed them, check out February’s hot startups here.

Amino Apps (New York, NY)
Amino Logo
Amino Apps was founded on the belief that interest-based communities were underdeveloped and outdated, particularly when it came to mobile. CEO Ben Anderson and CTO Yin Wang created the app to give users access to hundreds of thousands of communities, each of them a complete social network dedicated to a single topic. Some of the largest communities have over 1 million members and are built around topics like popular TV shows, video games, sports, and an endless number of hobbies and other interests. Amino hosts communities from around the world and is currently available in six languages with many more on the way.

Navigating the Amino app is easy. Simply download the app (iOS or Android), sign up with a valid email address, choose a profile picture, and start exploring. Users can search for communities and join any that fit their interests. Each community has chatrooms, multimedia content, quizzes, and a seamless commenting system. If a community doesn’t exist yet, users can create it in minutes using the Amino Creator and Manager app (ACM). The largest user-generated communities are turned into their own apps, which gives communities their own piece of real estate on members’ phones, as well as in app stores.

Amino’s vast global network of hundreds of thousands of communities is run on AWS services. Every day users generate, share, and engage with an enormous amount of content across hundreds of mobile applications. By leveraging AWS services including Amazon EC2, Amazon RDS, Amazon S3, Amazon SQS, and Amazon CloudFront, Amino can continue to provide new features to their users while scaling their service capacity to keep up with user growth.

Interested in joining Amino? Check out their jobs page here.

Appboy (New York, NY)
In 2011, Bill Magnuson, Jon Hyman, and Mark Ghermezian saw a unique opportunity to strengthen and humanize relationships between brands and their customers through technology. The trio created Appboy to empower brands to build long-term relationships with their customers and today they are the leading lifecycle engagement platform for marketing, growth, and engagement teams. The team recognized that as rapid mobile growth became undeniable, many brands were becoming frustrated with the lack of compelling and seamless cross-channel experiences offered by existing marketing clouds. Many of today’s top mobile apps and enterprise companies trust Appboy to take their marketing to the next level. Appboy manages user profiles for nearly 700 million monthly active users, and is used to power more than 10 billion personalized messages monthly across a multitude of channels and devices.

Appboy creates a holistic user profile that offers a single view of each customer. That user profile in turn powers contextual cross-channel messaging, lifecycle engagement automation, and robust campaign insights and optimization opportunities. Appboy offers solutions that allow brands to create push notifications, targeted emails, in-app and in-browser messages, news feed cards, and webhooks to enhance the user experience and increase customer engagement. The company prides itself on its interoperability, connecting to a variety of complimentary marketing tools and technologies so brands can build the perfect stack to enable their strategies and experiments in real time.

AWS makes it easy for Appboy to dynamically size all of their service components and automatically scale up and down as needed. They use an array of services including Elastic Load Balancing, AWS Lambda, Amazon CloudWatch, Auto Scaling groups, and Amazon S3 to help scale capacity and better deal with unpredictable customer loads.

To keep up with the latest marketing trends and tactics, visit the Appboy digital magazine, Relate. Appboy was also recently featured in the #StartupsOnAir video series where they gave insight into their AWS usage.

Arterys (San Francisco, CA)
Getting test results back from a physician can often be a time consuming and tedious process. Clinicians typically employ a variety of techniques to manually measure medical images and then make their assessments. Arterys founders Fabien Beckers, John Axerio-Cilies, Albert Hsiao, and Shreyas Vasanawala realized that much more computation and advanced analytics were needed to harness all of the valuable information in medical images, especially those generated by MRI and CT scanners. Clinicians were often skipping measurements and making assessments based mostly on qualitative data. Their solution was to start a cloud/AI software company focused on accelerating data-driven medicine with advanced software products for post-processing of medical images.

Arterys’ products provide timely, accurate, and consistent quantification of images, improve speed to results, and improve the quality of the information offered to the treating physician. This allows for much better tracking of a patient’s condition, and thus better decisions about their care. Advanced analytics, such as deep learning and distributed cloud computing, are used to process images. The first Arterys product can contour cardiac anatomy as accurately as experts, but takes only 15-20 seconds instead of the 45-60 minutes required to do it manually. Their computing cloud platform is also fully HIPAA compliant.

Arterys relies on a variety of AWS services to process their medical images. Using deep learning and other advanced analytic tools, Arterys is able to render images without latency over a web browser using AWS G2 instances. They use Amazon EC2 extensively for all of their compute needs, including inference and rendering, and Amazon S3 is used to archive images that aren’t needed immediately, as well as manage costs. Arterys also employs Amazon Route 53, AWS CloudTrail, and Amazon EC2 Container Service.

Check out this quick video about the technology that Arterys is creating. They were also recently featured in the #StartupsOnAir video series and offered a quick demo of their product.

Protenus (Baltimore, MD)
Protenus Logo
Protenus founders Nick Culbertson and Robert Lord were medical students at Johns Hopkins Medical School when they saw first-hand how Electronic Health Record (EHR) systems could be used to improve patient care and share clinical data more efficiently. With increased efficiency came a huge issue – an onslaught of serious security and privacy concerns. Over the past two years, 140 million medical records have been breached, meaning that approximately 1 in 3 Americans have had their health data compromised. Health records contain a repository of sensitive information and a breach of that data can cause major havoc in a patient’s life – namely identity theft, prescription fraud, Medicare/Medicaid fraud, and improper performance of medical procedures. Using their experience and knowledge from former careers in the intelligence community and involvement in a leading hedge fund, Nick and Robert developed the prototype and algorithms that launched Protenus.

Today, Protenus offers a number of solutions that detect breaches and misuse of patient data for healthcare organizations nationwide. Using advanced analytics and AI, Protenus’ health data insights platform understands appropriate vs. inappropriate use of patient data in the EHR. It also protects privacy, aids compliance with HIPAA regulations, and ensures trust for patients and providers alike.

Protenus built and operates its SaaS offering atop Amazon EC2, where Dedicated Hosts and encrypted Amazon EBS volume are used to ensure compliance with HIPAA regulation for the storage of Protected Health Information. They use Elastic Load Balancing and Amazon Route 53 for DNS, enabling unique, secure client specific access points to their Protenus instance.

To learn more about threats to patient data, read Hospitals’ Biggest Threat to Patient Data is Hiding in Plain Sight on the Protenus blog. Also be sure to check out their recent video in the #StartupsOnAir series for more insight into their product.

Syapse (Palo Alto, CA)
Syapse provides a comprehensive software solution that enables clinicians to treat patients with precision medicine for targeted cancer therapies — treatments that are designed and chosen using genetic or molecular profiling. Existing hospital IT doesn’t support the robust infrastructure and clinical workflows required to treat patients with precision medicine at scale, but Syapse centralizes and organizes patient data to clinicians at the point of care. Syapse offers a variety of solutions for oncologists that allow them to access the full scope of patient data longitudinally, view recommended treatments or clinical trials for similar patients, and track outcomes over time. These solutions are helping health systems across the country to improve patient outcomes by offering the most innovative care to cancer patients.

Leading health systems such as Stanford Health Care, Providence St. Joseph Health, and Intermountain Healthcare are using Syapse to improve patient outcomes, streamline clinical workflows, and scale their precision medicine programs. A group of experts known as the Molecular Tumor Board (MTB) reviews complex cases and evaluates patient data, documents notes, and disseminates treatment recommendations to the treating physician. Syapse also provides reports that give health system staff insight into their institution’s oncology care, which can be used toward quality improvement, business goals, and understanding variables in the oncology service line.

Syapse uses Amazon Virtual Private Cloud, Amazon EC2 Dedicated Instances, and Amazon Elastic Block Store to build a high-performance, scalable, and HIPAA-compliant data platform that enables health systems to make precision medicine part of routine cancer care for patients throughout the country.

Be sure to check out the Syapse blog to learn more and also their recent video on the #StartupsOnAir video series where they discuss their product, HIPAA compliance, and more about how they are using AWS.

Thank you for checking out another month of awesome hot startups!

-Tina Barr