Tag Archives: Demo

Announcing the new AWS Certified Security – Specialty exam

Post Syndicated from Janna Pellegrino original https://aws.amazon.com/blogs/architecture/announcing-the-new-aws-certified-security-specialty-exam/

Good news for cloud security experts: following our most popular beta exam ever, the AWS Certified Security – Specialty exam is here. This new exam allows experienced cloud security professionals to demonstrate and validate their knowledge of how to secure the AWS platform.

About the exam
The security exam covers incident response, logging and monitoring, infrastructure security, identity and access management, and data protection. The exam is open to anyone who currently holds a Cloud Practitioner or Associate-level certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

The exam validates:

  • An understanding of specialized data classifications and AWS data protection mechanisms.
  • An understanding of data encryption methods and AWS mechanisms to implement them.
  • An understanding of secure Internet protocols and AWS mechanisms to implement them.
  • A working knowledge of AWS security services and features of services to provide a secure production environment.
  • Competency gained from two or more years of production deployment experience using AWS security services and features.
  • Ability to make trade-off decisions with regard to cost, security, and deployment complexity given a set of application requirements.
  • An understanding of security operations and risk.

Learn more and register >>

How to prepare
We have training and other resources to help you prepare for the exam:

AWS Training (aws.amazon.com/training)

Additional Resources

Learn more and register >>

Please contact us if you have questions about exam registration.

Good luck!

Announcing the new AWS Certified Security – Specialty exam

Post Syndicated from Ozlem Yilmaz original https://aws.amazon.com/blogs/security/announcing-the-new-aws-certified-security-specialty-exam/

Good news for cloud security experts: the AWS Certified Security — Specialty exam is here. This new exam allows experienced cloud security professionals to demonstrate and validate their knowledge of how to secure the AWS platform.

About the exam

The security exam covers incident response, logging and monitoring, infrastructure security, identity and access management, and data protection. The exam is open to anyone who currently holds a Cloud Practitioner or Associate-level certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

The exam validates your understanding of:

  • Specialized data classifications and AWS data protection mechanisms
  • Data encryption methods and AWS mechanisms to implement them
  • Secure Internet protocols and AWS mechanisms to implement them
  • AWS security services and features of services to provide a secure production environment
  • Making tradeoff decisions with regard to cost, security, and deployment complexity given a set of application requirements
  • Security operations and risk

How to prepare

We have training and other resources to help you prepare for the exam.

AWS Training that includes:

Additional Resources

Learn more and register here, and please contact us if you have questions about exam registration.

Want more AWS Security news? Follow us on Twitter.

Invent new sounds with Google’s NSynth Super

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/google-nsynth-super/

Discover new sounds and explore the role of machine learning in music production and sound research with the NSynth Super, an ongoing project from Google’s Magenta research team that you can build at home.

Google Open NSynth Super Testing

Uploaded by AB Open on 2018-04-17.

What is the NSynth Super?

Part of the ongoing Magenta research project within Google, NSynth Super explores the ways in which machine learning tools help artists and musicians be creative.

Google Nsynth Super Raspberry Pi

“Technology has always played a role in creating new types of sounds that inspire musicians — from the sounds of distortion to the electronic sounds of synths,” explains the team behind the NSynth Super. “Today, advances in machine learning and neural networks have opened up new possibilities for sound generation.”

Using TensorFlow, the Magenta team builds tools and interfaces that let  artists and musicians use machine learning in their work. The NSynth Super AI algorithm uses deep neural networking to investigate the character of sounds. It then builds new sounds based on these characteristics instead of simply mixing sounds together.

Using an autoencoder, it extracts 16 defining temporal features from each input. These features are then interpolated linearly to create new embeddings (mathematical representations of each sound). These new embeddings are then decoded into new sounds, which have the acoustic qualities of both inputs.

The team publishes all hardware designs and software that are part of their ongoing research under open-source licences, allowing you to build your own synth.

Build your own NSynth Super

Using these open-source tools, Andrew Black has produced his own NSynth Super, demoed in the video above. Andrew’s list of build materials includes a Raspberry Pi 3, potentiometers, rotary encoders, and the Adafruit 1.3″ OLED display. Magenta also provides Gerber files for you to fabricate your own PCB.

Google Nsynth Super Raspberry Pi

Once fabricated, the PCB includes a table of contents for adding components.

The build isn’t easy — it requires soldering skills or access to someone who can assemble PCBs. Take a look at Andrew’s blog post and the official NSynth GitHub repo to see whether you’re up to the challenge.

Google Nsynth Super Raspberry Pi
Google Nsynth Super Raspberry Pi
Google Nsynth Super Raspberry Pi

Music and Raspberry Pi

The Raspberry Pi has been widely used for music production and music builds. Be it retrofitting a boombox, distributing music atop Table Mountain, or coding tracks with Sonic Pi, the Pi offers endless opportunities for musicians and music lovers to expand their repertoire of builds and instruments.

If you’d like to try more music-based projects using the Raspberry Pi, you can check out our free resources. And if you’ve used a Raspberry Pi in your own musical project, please share it with us in the comments or via our social network accounts.

The post Invent new sounds with Google’s NSynth Super appeared first on Raspberry Pi.

The Pirate Bay Suffers Extended Downtime, Tor Domain Is Up

Post Syndicated from Ernesto original https://torrentfreak.com/pirate-bay-suffers-extended-downtime-tor-domain/

pirate bayThe main Pirate Bay domain has been offline for the last one-and-a-half days.

For most people, the site currently displays a Cloudflare error message across the entire site, with the CDN provider referring to a “bad gateway.”

No further details are available to us and there is no known ETA for the site’s full return. Judging from past experience, however, it’s likely a small technical hiccup that needs fixing.

There are no issues with the domain name itself and Cloudflare seems to be fully functional as well.

Pirate Bay downtime, bad gateway

TorrentFreak hasn’t heard anything from the TPB team but these type of outages are not unusual. The Pirate Bay has had quite a few stints of downtime in recent months. The popular torrent site usually returns after several hours.

Amid the downtime, there’s still some good news for those who desperately need to access the notorious torrent site. TPB is still available via its .onion address on the Tor network, accessible using the popular Tor Browser, for example.

The site’s Tor traffic goes through a separate server and works just fine. However, based on the irregular uploads, that’s not going completely smoothly either.

In addition, some of The Pirate Bay’s unofficial proxy sites are still working fine and showing new torrents.

As always, more details on The Pirate Bay’s current status are available on the official forum, but don’t expect any ETA there.

“Patience is the game we are all playing for now,” TPB moderator demonS notes.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

Implement continuous integration and delivery of serverless AWS Glue ETL applications using AWS Developer Tools

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/implement-continuous-integration-and-delivery-of-serverless-aws-glue-etl-applications-using-aws-developer-tools/

AWS Glue is an increasingly popular way to develop serverless ETL (extract, transform, and load) applications for big data and data lake workloads. Organizations that transform their ETL applications to cloud-based, serverless ETL architectures need a seamless, end-to-end continuous integration and continuous delivery (CI/CD) pipeline: from source code, to build, to deployment, to product delivery. Having a good CI/CD pipeline can help your organization discover bugs before they reach production and deliver updates more frequently. It can also help developers write quality code and automate the ETL job release management process, mitigate risk, and more.

AWS Glue is a fully managed data catalog and ETL service. It simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. AWS Glue crawls your data sources and constructs a data catalog using pre-built classifiers for popular data formats and data types, including CSV, Apache Parquet, JSON, and more.

When you are developing ETL applications using AWS Glue, you might come across some of the following CI/CD challenges:

  • Iterative development with unit tests
  • Continuous integration and build
  • Pushing the ETL pipeline to a test environment
  • Pushing the ETL pipeline to a production environment
  • Testing ETL applications using real data (live test)
  • Exploring and validating data

In this post, I walk you through a solution that implements a CI/CD pipeline for serverless AWS Glue ETL applications supported by AWS Developer Tools (including AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild) and AWS CloudFormation.

Solution overview

The following diagram shows the pipeline workflow:

This solution uses AWS CodePipeline, which lets you orchestrate and automate the test and deploy stages for ETL application source code. The solution consists of a pipeline that contains the following stages:

1.) Source Control: In this stage, the AWS Glue ETL job source code and the AWS CloudFormation template file for deploying the ETL jobs are both committed to version control. I chose to use AWS CodeCommit for version control.

To get the ETL job source code and AWS CloudFormation template, download the gluedemoetl.zip file. This solution is developed based on a previous post, Build a Data Lake Foundation with AWS Glue and Amazon S3.

2.) LiveTest: In this stage, all resources—including AWS Glue crawlers, jobs, S3 buckets, roles, and other resources that are required for the solution—are provisioned, deployed, live tested, and cleaned up.

The LiveTest stage includes the following actions:

  • Deploy: In this action, all the resources that are required for this solution (crawlers, jobs, buckets, roles, and so on) are provisioned and deployed using an AWS CloudFormation template.
  • AutomatedLiveTest: In this action, all the AWS Glue crawlers and jobs are executed and data exploration and validation tests are performed. These validation tests include, but are not limited to, record counts in both raw tables and transformed tables in the data lake and any other business validations. I used AWS CodeBuild for this action.
  • LiveTestApproval: This action is included for the cases in which a pipeline administrator approval is required to deploy/promote the ETL applications to the next stage. The pipeline pauses in this action until an administrator manually approves the release.
  • LiveTestCleanup: In this action, all the LiveTest stage resources, including test crawlers, jobs, roles, and so on, are deleted using the AWS CloudFormation template. This action helps minimize cost by ensuring that the test resources exist only for the duration of the AutomatedLiveTest and LiveTestApproval

3.) DeployToProduction: In this stage, all the resources are deployed using the AWS CloudFormation template to the production environment.

Try it out

This code pipeline takes approximately 20 minutes to complete the LiveTest test stage (up to the LiveTest approval stage, in which manual approval is required).

To get started with this solution, choose Launch Stack:

This creates the CI/CD pipeline with all of its stages, as described earlier. It performs an initial commit of the sample AWS Glue ETL job source code to trigger the first release change.

In the AWS CloudFormation console, choose Create. After the template finishes creating resources, you see the pipeline name on the stack Outputs tab.

After that, open the CodePipeline console and select the newly created pipeline. Initially, your pipeline’s CodeCommit stage shows that the source action failed.

Allow a few minutes for your new pipeline to detect the initial commit applied by the CloudFormation stack creation. As soon as the commit is detected, your pipeline starts. You will see the successful stage completion status as soon as the CodeCommit source stage runs.

In the CodeCommit console, choose Code in the navigation pane to view the solution files.

Next, you can watch how the pipeline goes through the LiveTest stage of the deploy and AutomatedLiveTest actions, until it finally reaches the LiveTestApproval action.

At this point, if you check the AWS CloudFormation console, you can see that a new template has been deployed as part of the LiveTest deploy action.

At this point, make sure that the AWS Glue crawlers and the AWS Glue job ran successfully. Also check whether the corresponding databases and external tables have been created in the AWS Glue Data Catalog. Then verify that the data is validated using Amazon Athena, as shown following.

Open the AWS Glue console, and choose Databases in the navigation pane. You will see the following databases in the Data Catalog:

Open the Amazon Athena console, and run the following queries. Verify that the record counts are matching.

SELECT count(*) FROM "nycitytaxi_gluedemocicdtest"."data";
SELECT count(*) FROM "nytaxiparquet_gluedemocicdtest"."datalake";

The following shows the raw data:

The following shows the transformed data:

The pipeline pauses the action until the release is approved. After validating the data, manually approve the revision on the LiveTestApproval action on the CodePipeline console.

Add comments as needed, and choose Approve.

The LiveTestApproval stage now appears as Approved on the console.

After the revision is approved, the pipeline proceeds to use the AWS CloudFormation template to destroy the resources that were deployed in the LiveTest deploy action. This helps reduce cost and ensures a clean test environment on every deployment.

Production deployment is the final stage. In this stage, all the resources—AWS Glue crawlers, AWS Glue jobs, Amazon S3 buckets, roles, and so on—are provisioned and deployed to the production environment using the AWS CloudFormation template.

After successfully running the whole pipeline, feel free to experiment with it by changing the source code stored on AWS CodeCommit. For example, if you modify the AWS Glue ETL job to generate an error, it should make the AutomatedLiveTest action fail. Or if you change the AWS CloudFormation template to make its creation fail, it should affect the LiveTest deploy action. The objective of the pipeline is to guarantee that all changes that are deployed to production are guaranteed to work as expected.

Conclusion

In this post, you learned how easy it is to implement CI/CD for serverless AWS Glue ETL solutions with AWS developer tools like AWS CodePipeline and AWS CodeBuild at scale. Implementing such solutions can help you accelerate ETL development and testing at your organization.

If you have questions or suggestions, please comment below.

 


Additional Reading

If you found this post useful, be sure to check out Implement Continuous Integration and Delivery of Apache Spark Applications using AWS and Build a Data Lake Foundation with AWS Glue and Amazon S3.

 


About the Authors

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.

 
Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.

 

 

 

Securing Elections

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/04/securing_electi_1.html

Elections serve two purposes. The first, and obvious, purpose is to accurately choose the winner. But the second is equally important: to convince the loser. To the extent that an election system is not transparently and auditably accurate, it fails in that second purpose. Our election systems are failing, and we need to fix them.

Today, we conduct our elections on computers. Our registration lists are in computer databases. We vote on computerized voting machines. And our tabulation and reporting is done on computers. We do this for a lot of good reasons, but a side effect is that elections now have all the insecurities inherent in computers. The only way to reliably protect elections from both malice and accident is to use something that is not hackable or unreliable at scale; the best way to do that is to back up as much of the system as possible with paper.

Recently, there have been two graphic demonstrations of how bad our computerized voting system is. In 2007, the states of California and Ohio conducted audits of their electronic voting machines. Expert review teams found exploitable vulnerabilities in almost every component they examined. The researchers were able to undetectably alter vote tallies, erase audit logs, and load malware on to the systems. Some of their attacks could be implemented by a single individual with no greater access than a normal poll worker; others could be done remotely.

Last year, the Defcon hackers’ conference sponsored a Voting Village. Organizers collected 25 pieces of voting equipment, including voting machines and electronic poll books. By the end of the weekend, conference attendees had found ways to compromise every piece of test equipment: to load malicious software, compromise vote tallies and audit logs, or cause equipment to fail.

It’s important to understand that these were not well-funded nation-state attackers. These were not even academics who had been studying the problem for weeks. These were bored hackers, with no experience with voting machines, playing around between parties one weekend.

It shouldn’t be any surprise that voting equipment, including voting machines, voter registration databases, and vote tabulation systems, are that hackable. They’re computers — often ancient computers running operating systems no longer supported by the manufacturers — and they don’t have any magical security technology that the rest of the industry isn’t privy to. If anything, they’re less secure than the computers we generally use, because their manufacturers hide any flaws behind the proprietary nature of their equipment.

We’re not just worried about altering the vote. Sometimes causing widespread failures, or even just sowing mistrust in the system, is enough. And an election whose results are not trusted or believed is a failed election.

Voting systems have another requirement that makes security even harder to achieve: the requirement for a secret ballot. Because we have to securely separate the election-roll system that determines who can vote from the system that collects and tabulates the votes, we can’t use the security systems available to banking and other high-value applications.

We can securely bank online, but can’t securely vote online. If we could do away with anonymity — if everyone could check that their vote was counted correctly — then it would be easy to secure the vote. But that would lead to other problems. Before the US had the secret ballot, voter coercion and vote-buying were widespread.

We can’t, so we need to accept that our voting systems are insecure. We need an election system that is resilient to the threats. And for many parts of the system, that means paper.

Let’s start with the voter rolls. We know they’ve already been targeted. In 2016, someone changed the party affiliation of hundreds of voters before the Republican primary. That’s just one possibility. A well-executed attack that deletes, for example, one in five voters at random — or changes their addresses — would cause chaos on election day.

Yes, we need to shore up the security of these systems. We need better computer, network, and database security for the various state voter organizations. We also need to better secure the voter registration websites, with better design and better internet security. We need better security for the companies that build and sell all this equipment.

Multiple, unchangeable backups are essential. A record of every addition, deletion, and change needs to be stored on a separate system, on write-only media like a DVD. Copies of that DVD, or — even better — a paper printout of the voter rolls, should be available at every polling place on election day. We need to be ready for anything.

Next, the voting machines themselves. Security researchers agree that the gold standard is a voter-verified paper ballot. The easiest (and cheapest) way to achieve this is through optical-scan voting. Voters mark paper ballots by hand; they are fed into a machine and counted automatically. That paper ballot is saved, and serves as a final true record in a recount in case of problems. Touch-screen machines that print a paper ballot to drop in a ballot box can also work for voters with disabilities, as long as the ballot can be easily read and verified by the voter.

Finally, the tabulation and reporting systems. Here again we need more security in the process, but we must always use those paper ballots as checks on the computers. A manual, post-election, risk-limiting audit varies the number of ballots examined according to the margin of victory. Conducting this audit after every election, before the results are certified, gives us confidence that the election outcome is correct, even if the voting machines and tabulation computers have been tampered with. Additionally, we need better coordination and communications when incidents occur.

It’s vital to agree on these procedures and policies before an election. Before the fact, when anyone can win and no one knows whose votes might be changed, it’s easy to agree on strong security. But after the vote, someone is the presumptive winner — and then everything changes. Half of the country wants the result to stand, and half wants it reversed. At that point, it’s too late to agree on anything.

The politicians running in the election shouldn’t have to argue their challenges in court. Getting elections right is in the interest of all citizens. Many countries have independent election commissions that are charged with conducting elections and ensuring their security. We don’t do that in the US.

Instead, we have representatives from each of our two parties in the room, keeping an eye on each other. That provided acceptable security against 20th-century threats, but is totally inadequate to secure our elections in the 21st century. And the belief that the diversity of voting systems in the US provides a measure of security is a dangerous myth, because few districts can be decisive and there are so few voting-machine vendors.

We can do better. In 2017, the Department of Homeland Security declared elections to be critical infrastructure, allowing the department to focus on securing them. On 23 March, Congress allocated $380m to states to upgrade election security.

These are good starts, but don’t go nearly far enough. The constitution delegates elections to the states but allows Congress to “make or alter such Regulations”. In 1845, Congress set a nationwide election day. Today, we need it to set uniform and strict election standards.

This essay originally appeared in the Guardian.

Colour sensing with a Raspberry Pi

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/colour-sensing-raspberry-pi/

In their latest video and tutorial, Electronic Hub shows you how to detect colour using a Raspberry Pi and a TCS3200 colour sensor.

Raspberry Pi Color Sensor (TCS3200) Interface | Color Detector

A simple Raspberry Pi based project using TCS3200 Color Sensor. The project demonstrates how to interface a Color Sensor (like TCS3200) with Raspberry Pi and implement a simple Color Detector using Raspberry Pi.

What is a TCS3200 colour sensor?

Colour sensors sense reflected light from nearby objects. The bright light of the TCS3200’s on-board white LEDs hits an object’s surface and is reflected back. The sensor has an 8×8 array of photodiodes, which are covered by either a red, blue, green, or clear filter. The type of filter determines what colour a diode can detect. Then the overall colour of an object is determined by how much light of each colour it reflects. (For example, a red object reflects mostly red light.)

Colour sensing with the TCS3200 Color Sensor and a Raspberry Pi

As Electronics Hub explains:

TCS3200 is one of the easily available colour sensors that students and hobbyists can work on. It is basically a light-to-frequency converter, i.e. based on colour and intensity of the light falling on it, the frequency of its output signal varies.

I’ll save you a physics lesson here, but you can find a detailed explanation of colour sensing and the TCS3200 on the Electronics Hub blog.

Raspberry Pi colour sensor

The TCS3200 colour sensor is connected to several of the onboard General Purpose Input Output (GPIO) pins on the Raspberry Pi.

Colour sensing with the TCS3200 Color Sensor and a Raspberry Pi

These connections allow the Raspberry Pi 3 to run one of two Python scripts that Electronics Hub has written for the project. The first displays the RAW RGB values read by the sensor. The second detects the primary colours red, green, and blue, and it can be expanded for more colours with the help of the first script.

Colour sensing with the TCS3200 Color Sensor and a Raspberry Pi

Electronic Hub’s complete build uses a breadboard for simply prototyping

Use it in your projects

This colour sensing setup is a simple means of adding a new dimension to your builds. Why not build a candy-sorting robot that organises your favourite sweets by colour? Or add colour sensing to your line-following buggy to allow for multiple path options!

If your Raspberry Pi project uses colour sensing, we’d love to see it, so be sure to share it in the comments!

The post Colour sensing with a Raspberry Pi appeared first on Raspberry Pi.

Now You Can Create Encrypted Amazon EBS Volumes by Using Your Custom Encryption Keys When You Launch an Amazon EC2 Instance

Post Syndicated from Nishit Nagar original https://aws.amazon.com/blogs/security/create-encrypted-amazon-ebs-volumes-custom-encryption-keys-launch-amazon-ec2-instance-2/

Amazon Elastic Block Store (EBS) offers an encryption solution for your Amazon EBS volumes so you don’t have to build, maintain, and secure your own infrastructure for managing encryption keys for block storage. Amazon EBS encryption uses AWS Key Management Service (AWS KMS) customer master keys (CMKs) when creating encrypted Amazon EBS volumes, providing you all the benefits associated with using AWS KMS. You can specify either an AWS managed CMK or a customer-managed CMK to encrypt your Amazon EBS volume. If you use a customer-managed CMK, you retain granular control over your encryption keys, such as having AWS KMS rotate your CMK every year. To learn more about creating CMKs, see Creating Keys.

In this post, we demonstrate how to create an encrypted Amazon EBS volume using a customer-managed CMK when you launch an EC2 instance from the EC2 console, AWS CLI, and AWS SDK.

Creating an encrypted Amazon EBS volume from the EC2 console

Follow these steps to launch an EC2 instance from the EC2 console with Amazon EBS volumes that are encrypted by customer-managed CMKs:

  1. Sign in to the AWS Management Console and open the EC2 console.
  2. Select Launch instance, and then, in Step 1 of the wizard, select an Amazon Machine Image (AMI).
  3. In Step 2 of the wizard, select an instance type, and then provide additional configuration details in Step 3. For details about configuring your instances, see Launching an Instance.
  4. In Step 4 of the wizard, specify additional EBS volumes that you want to attach to your instances.
  5. To create an encrypted Amazon EBS volume, first add a new volume by selecting Add new volume. Leave the Snapshot column blank.
  6. In the Encrypted column, select your CMK from the drop-down menu. You can also paste the full Amazon Resource Name (ARN) of your custom CMK key ID in this box. To learn more about finding the ARN of a CMK, see Working with Keys.
  7. Select Review and Launch. Your instance will launch with an additional Amazon EBS volume with the key that you selected. To learn more about the launch wizard, see Launching an Instance with Launch Wizard.

Creating Amazon EBS encrypted volumes from the AWS CLI or SDK

You also can use RunInstances to launch an instance with additional encrypted Amazon EBS volumes by setting Encrypted to true and adding kmsKeyID along with the actual key ID in the BlockDeviceMapping object, as shown in the following command:

$> aws ec2 run-instances –image-id ami-b42209de –count 1 –instance-type m4.large –region us-east-1 –block-device-mappings file://mapping.json

In this example, mapping.json describes the properties of the EBS volume that you want to create:


{
"DeviceName": "/dev/sda1",
"Ebs": {
"DeleteOnTermination": true,
"VolumeSize": 100,
"VolumeType": "gp2",
"Encrypted": true,
"kmsKeyID": "arn:aws:kms:us-east-1:012345678910:key/abcd1234-a123-456a-a12b-a123b4cd56ef"
}
}

You can also launch instances with additional encrypted EBS data volumes via an Auto Scaling or Spot Fleet by creating a launch template with the above BlockDeviceMapping. For example:

$> aws ec2 create-launch-template –MyLTName –image-id ami-b42209de –count 1 –instance-type m4.large –region us-east-1 –block-device-mappings file://mapping.json

To learn more about launching an instance with the AWS CLI or SDK, see the AWS CLI Command Reference.

In this blog post, we’ve demonstrated a single-step, streamlined process for creating Amazon EBS volumes that are encrypted under your CMK when you launch your EC2 instance, thereby streamlining your instance launch workflow. To start using this functionality, navigate to the EC2 console.

If you have feedback about this blog post, submit comments in the Comments section below. If you have questions about this blog post, start a new thread on the Amazon EC2 forum or contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

The DMCA and its Chilling Effects on Research

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

The Center for Democracy and Technology has a good summary of the current state of the DMCA’s chilling effects on security research.

To underline the nature of chilling effects on hacking and security research, CDT has worked to describe how tinkerers, hackers, and security researchers of all types both contribute to a baseline level of security in our digital environment and, in turn, are shaped themselves by this environment, most notably when things they do upset others and result in threats, potential lawsuits, and prosecution. We’ve published two reports (sponsored by the Hewlett Foundation and MacArthur Foundation) about needed reforms to the law and the myriad of ways that security research directly improves people’s lives. To get a more complete picture, we wanted to talk to security researchers themselves and gauge the forces that shape their work; essentially, we wanted to “take the pulse” of the security research community.

Today, we are releasing a third report in service of this effort: “Taking the Pulse of Hacking: A Risk Basis for Security Research.” We report findings after having interviewed a set of 20 security researchers and hackers — half academic and half non-academic — about what considerations they take into account when starting new projects or engaging in new work, as well as to what extent they or their colleagues have faced threats in the past that chilled their work. The results in our report show that a wide variety of constraints shape the work they do, from technical constraints to ethical boundaries to legal concerns, including the DMCA and especially the CFAA.

Note: I am a signatory on the letter supporting unrestricted security research.

Pirate Site-Blocking? Music Biz Wants App Blocking Too

Post Syndicated from Andy original https://torrentfreak.com/pirate-site-blocking-music-biz-wants-app-blocking-too-180415/

In some way, shape or form, Internet piracy has always been carried out through some kind of application. Whether that’s a peer-to-peer client utilizing BitTorrent or eD2K, or a Usenet or FTP tool taking things back to their roots, software has always played a crucial role.

Of course, the nature of the Internet beast means that software usage is unavoidable but in recent years piracy has swung more towards the regular web browser, meaning that sites and services offering pirated content are largely easy to locate, identify and block, if authorities so choose.

As revealed this week by the MPA, thousands of platforms around the world are now targeted for blocking, with 1,800 sites and 5,300 domains blocked in Europe alone.

However, as the Kodi phenomenon has shown, web-based content doesn’t always have to be accessed via a standard web browser. Clever but potentially illegal addons and third-party apps are able to scrape web-based resources and present links to content on a wide range of devices, from mobile phones and tablets to set-top boxes.

While it’s still possible to block the resources upon which these addons rely, the scattered nature of the content makes the process much more difficult. One can’t simply block a whole platform because a few movies are illegally hosted there and even Google has found itself hosting thousands of infringing titles, a situation that’s ruthlessly exploited by addon and app developers alike.

Needless to say, the situation hasn’t gone unnoticed. The Alliance for Creativity and Entertainment has spent the last year (1,2,3) targeting many people involved in the addon and app scene, hoping they’ll take their tools and run, rather than further develop a rapidly evolving piracy ecosystem.

Over in Russia, a country that will happily block hundreds or millions of IP addresses if it suits them, the topic of infringing apps was raised this week. It happened during the International Strategic Forum on Intellectual Property, a gathering of 500 experts from more than 30 countries. There were strong calls for yet more tools and measures to deal with films and music being made available via ‘pirate’ apps.

The forum heard that in response to widespread website blocking, people behind pirate sites have begun creating applications for mobile devices to achieve the same ends – the provision of illegal content. This, key players in the music industry say, means that the law needs to be further tightened to tackle the rising threat.

“Consumption of content is now going into the mobile sector and due to this we plan to prevent mass migration of ‘pirates’ to the mobile sector,” said Leonid Agronov, general director of the National Federation of the Music Industry.

The same concerns were echoed by Alexander Blinov, CEO of Warner Music Russia. According to TASS, the powerful industry player said that while recent revenues had been positively affected by site-blocking, it’s now time to start taking more action against apps.

“I agree with all speakers that we can not stop at what has been achieved so far. The music industry has a fight against illegal content in mobile applications on the agenda,” Blinov said.

And if Blinov is to be believed, music in Russia is doing particularly well at the moment. Attributing successes to efforts by parliament, the Ministry of Communications, and copyright holders, Blinov said the local music market has doubled in the past two years.

“We are now in the top three fastest growing markets in the world, behind only China and South Korea,” Blinov said.

While some apps can work in the same manner as a basic web interface, others rely on more complex mechanisms, ‘scraping’ content from diverse sources that can be easily and readily changed if mitigation measures kick in. It will be very interesting to see how Russia deals with this threat and whether it will opt for highly technical solutions or the nuclear options demonstrated recently.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

AWS AppSync – Production-Ready with Six New Features

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-appsync-production-ready-with-six-new-features/

If you build (or want to build) data-driven web and mobile apps and need real-time updates and the ability to work offline, you should take a look at AWS AppSync. Announced in preview form at AWS re:Invent 2017 and described in depth here, AWS AppSync is designed for use in iOS, Android, JavaScript, and React Native apps. AWS AppSync is built around GraphQL, an open, standardized query language that makes it easy for your applications to request the precise data that they need from the cloud.

I’m happy to announce that the preview period is over and that AWS AppSync is now generally available and production-ready, with six new features that will simplify and streamline your application development process:

Console Log Access – You can now see the CloudWatch Logs entries that are created when you test your GraphQL queries, mutations, and subscriptions from within the AWS AppSync Console.

Console Testing with Mock Data – You can now create and use mock context objects in the console for testing purposes.

Subscription Resolvers – You can now create resolvers for AWS AppSync subscription requests, just as you can already do for query and mutate requests.

Batch GraphQL Operations for DynamoDB – You can now make use of DynamoDB’s batch operations (BatchGetItem and BatchWriteItem) across one or more tables. in your resolver functions.

CloudWatch Support – You can now use Amazon CloudWatch Metrics and CloudWatch Logs to monitor calls to the AWS AppSync APIs.

CloudFormation Support – You can now define your schemas, data sources, and resolvers using AWS CloudFormation templates.

A Brief AppSync Review
Before diving in to the new features, let’s review the process of creating an AWS AppSync API, starting from the console. I click Create API to begin:

I enter a name for my API and (for demo purposes) choose to use the Sample schema:

The schema defines a collection of GraphQL object types. Each object type has a set of fields, with optional arguments:

If I was creating an API of my own I would enter my schema at this point. Since I am using the sample, I don’t need to do this. Either way, I click on Create to proceed:

The GraphQL schema type defines the entry points for the operations on the data. All of the data stored on behalf of a particular schema must be accessible using a path that begins at one of these entry points. The console provides me with an endpoint and key for my API:

It also provides me with guidance and a set of fully functional sample apps that I can clone:

When I clicked Create, AWS AppSync created a pair of Amazon DynamoDB tables for me. I can click Data Sources to see them:

I can also see and modify my schema, issue queries, and modify an assortment of settings for my API.

Let’s take a quick look at each new feature…

Console Log Access
The AWS AppSync Console already allows me to issue queries and to see the results, and now provides access to relevant log entries.In order to see the entries, I must enable logs (as detailed below), open up the LOGS, and check the checkbox. Here’s a simple mutation query that adds a new event. I enter the query and click the arrow to test it:

I can click VIEW IN CLOUDWATCH for a more detailed view:

To learn more, read Test and Debug Resolvers.

Console Testing with Mock Data
You can now create a context object in the console where it will be passed to one of your resolvers for testing purposes. I’ll add a testResolver item to my schema:

Then I locate it on the right-hand side of the Schema page and click Attach:

I choose a data source (this is for testing and the actual source will not be accessed), and use the Put item mapping template:

Then I click Select test context, choose Create New Context, assign a name to my test content, and click Save (as you can see, the test context contains the arguments from the query along with values to be returned for each field of the result):

After I save the new Resolver, I click Test to see the request and the response:

Subscription Resolvers
Your AWS AppSync application can monitor changes to any data source using the @aws_subscribe GraphQL schema directive and defining a Subscription type. The AWS AppSync client SDK connects to AWS AppSync using MQTT over Websockets and the application is notified after each mutation. You can now attach resolvers (which convert GraphQL payloads into the protocol needed by the underlying storage system) to your subscription fields and perform authorization checks when clients attempt to connect. This allows you to perform the same fine grained authorization routines across queries, mutations, and subscriptions.

To learn more about this feature, read Real-Time Data.

Batch GraphQL Operations
Your resolvers can now make use of DynamoDB batch operations that span one or more tables in a region. This allows you to use a list of keys in a single query, read records multiple tables, write records in bulk to multiple tables, and conditionally write or delete related records across multiple tables.

In order to use this feature the IAM role that you use to access your tables must grant access to DynamoDB’s BatchGetItem and BatchPutItem functions.

To learn more, read the DynamoDB Batch Resolvers tutorial.

CloudWatch Logs Support
You can now tell AWS AppSync to log API requests to CloudWatch Logs. Click on Settings and Enable logs, then choose the IAM role and the log level:

CloudFormation Support
You can use the following CloudFormation resource types in your templates to define AWS AppSync resources:

AWS::AppSync::GraphQLApi – Defines an AppSync API in terms of a data source (an Amazon Elasticsearch Service domain or a DynamoDB table).

AWS::AppSync::ApiKey – Defines the access key needed to access the data source.

AWS::AppSync::GraphQLSchema – Defines a GraphQL schema.

AWS::AppSync::DataSource – Defines a data source.

AWS::AppSync::Resolver – Defines a resolver by referencing a schema and a data source, and includes a mapping template for requests.

Here’s a simple schema definition in YAML form:

  AppSyncSchema:
    Type: "AWS::AppSync::GraphQLSchema"
    DependsOn:
      - AppSyncGraphQLApi
    Properties:
      ApiId: !GetAtt AppSyncGraphQLApi.ApiId
      Definition: |
        schema {
          query: Query
          mutation: Mutation
        }
        type Query {
          singlePost(id: ID!): Post
          allPosts: [Post]
        }
        type Mutation {
          putPost(id: ID!, title: String!): Post
        }
        type Post {
          id: ID!
          title: String!
        }

Available Now
These new features are available now and you can start using them today! Here are a couple of blog posts and other resources that you might find to be of interest:

Jeff;

 

 

How to retain system tables’ data spanning multiple Amazon Redshift clusters and run cross-cluster diagnostic queries

Post Syndicated from Karthik Sonti original https://aws.amazon.com/blogs/big-data/how-to-retain-system-tables-data-spanning-multiple-amazon-redshift-clusters-and-run-cross-cluster-diagnostic-queries/

Amazon Redshift is a data warehouse service that logs the history of the system in STL log tables. The STL log tables manage disk space by retaining only two to five days of log history, depending on log usage and available disk space.

To retain STL tables’ data for an extended period, you usually have to create a replica table for every system table. Then, for each you load the data from the system table into the replica at regular intervals. By maintaining replica tables for STL tables, you can run diagnostic queries on historical data from the STL tables. You then can derive insights from query execution times, query plans, and disk-spill patterns, and make better cluster-sizing decisions. However, refreshing replica tables with live data from STL tables at regular intervals requires schedulers such as Cron or AWS Data Pipeline. Also, these tables are specific to one cluster and they are not accessible after the cluster is terminated. This is especially true for transient Amazon Redshift clusters that last for only a finite period of ad hoc query execution.

In this blog post, I present a solution that exports system tables from multiple Amazon Redshift clusters into an Amazon S3 bucket. This solution is serverless, and you can schedule it as frequently as every five minutes. The AWS CloudFormation deployment template that I provide automates the solution setup in your environment. The system tables’ data in the Amazon S3 bucket is partitioned by cluster name and query execution date to enable efficient joins in cross-cluster diagnostic queries.

I also provide another CloudFormation template later in this post. This second template helps to automate the creation of tables in the AWS Glue Data Catalog for the system tables’ data stored in Amazon S3. After the system tables are exported to Amazon S3, you can run cross-cluster diagnostic queries on the system tables’ data and derive insights about query executions in each Amazon Redshift cluster. You can do this using Amazon QuickSight, Amazon Athena, Amazon EMR, or Amazon Redshift Spectrum.

You can find all the code examples in this post, including the CloudFormation templates, AWS Glue extract, transform, and load (ETL) scripts, and the resolution steps for common errors you might encounter in this GitHub repository.

Solution overview

The solution in this post uses AWS Glue to export system tables’ log data from Amazon Redshift clusters into Amazon S3. The AWS Glue ETL jobs are invoked at a scheduled interval by AWS Lambda. AWS Systems Manager, which provides secure, hierarchical storage for configuration data management and secrets management, maintains the details of Amazon Redshift clusters for which the solution is enabled. The last-fetched time stamp values for the respective cluster-table combination are maintained in an Amazon DynamoDB table.

The following diagram covers the key steps involved in this solution.

The solution as illustrated in the preceding diagram flows like this:

  1. The Lambda function, invoke_rs_stl_export_etl, is triggered at regular intervals, as controlled by Amazon CloudWatch. It’s triggered to look up the AWS Systems Manager parameter store to get the details of the Amazon Redshift clusters for which the system table export is enabled.
  2. The same Lambda function, based on the Amazon Redshift cluster details obtained in step 1, invokes the AWS Glue ETL job designated for the Amazon Redshift cluster. If an ETL job for the cluster is not found, the Lambda function creates one.
  3. The ETL job invoked for the Amazon Redshift cluster gets the cluster credentials from the parameter store. It gets from the DynamoDB table the last exported time stamp of when each of the system tables was exported from the respective Amazon Redshift cluster.
  4. The ETL job unloads the system tables’ data from the Amazon Redshift cluster into an Amazon S3 bucket.
  5. The ETL job updates the DynamoDB table with the last exported time stamp value for each system table exported from the Amazon Redshift cluster.
  6. The Amazon Redshift cluster system tables’ data is available in Amazon S3 and is partitioned by cluster name and date for running cross-cluster diagnostic queries.

Understanding the configuration data

This solution uses AWS Systems Manager parameter store to store the Amazon Redshift cluster credentials securely. The parameter store also securely stores other configuration information that the AWS Glue ETL job needs for extracting and storing system tables’ data in Amazon S3. Systems Manager comes with a default AWS Key Management Service (AWS KMS) key that it uses to encrypt the password component of the Amazon Redshift cluster credentials.

The following table explains the global parameters and cluster-specific parameters required in this solution. The global parameters are defined once and applicable at the overall solution level. The cluster-specific parameters are specific to an Amazon Redshift cluster and repeat for each cluster for which you enable this post’s solution. The CloudFormation template explained later in this post creates these parameters as part of the deployment process.

Parameter name Type Description
Global parametersdefined once and applied to all jobs
redshift_query_logs.global.s3_prefix String The Amazon S3 path where the query logs are exported. Under this path, each exported table is partitioned by cluster name and date.
redshift_query_logs.global.tempdir String The Amazon S3 path that AWS Glue ETL jobs use for temporarily staging the data.
redshift_query_logs.global.role> String The name of the role that the AWS Glue ETL jobs assume. Just the role name is sufficient. The complete Amazon Resource Name (ARN) is not required.
redshift_query_logs.global.enabled_cluster_list StringList A comma-separated list of cluster names for which system tables’ data export is enabled. This gives flexibility for a user to exclude certain clusters.
Cluster-specific parametersfor each cluster specified in the enabled_cluster_list parameter
redshift_query_logs.<<cluster_name>>.connection String The name of the AWS Glue Data Catalog connection to the Amazon Redshift cluster. For example, if the cluster name is product_warehouse, the entry is redshift_query_logs.product_warehouse.connection.
redshift_query_logs.<<cluster_name>>.user String The user name that AWS Glue uses to connect to the Amazon Redshift cluster.
redshift_query_logs.<<cluster_name>>.password Secure String The password that AWS Glue uses to connect the Amazon Redshift cluster’s encrypted-by key that is managed in AWS KMS.

For example, suppose that you have two Amazon Redshift clusters, product-warehouse and category-management, for which the solution described in this post is enabled. In this case, the parameters shown in the following screenshot are created by the solution deployment CloudFormation template in the AWS Systems Manager parameter store.

Solution deployment

To make it easier for you to get started, I created a CloudFormation template that automatically configures and deploys the solution—only one step is required after deployment.

Prerequisites

To deploy the solution, you must have one or more Amazon Redshift clusters in a private subnet. This subnet must have a network address translation (NAT) gateway or a NAT instance configured, and also a security group with a self-referencing inbound rule for all TCP ports. For more information about why AWS Glue ETL needs the configuration it does, described previously, see Connecting to a JDBC Data Store in a VPC in the AWS Glue documentation.

To start the deployment, launch the CloudFormation template:

CloudFormation stack parameters

The following table lists and describes the parameters for deploying the solution to export query logs from multiple Amazon Redshift clusters.

Property Default Description
S3Bucket mybucket The bucket this solution uses to store the exported query logs, stage code artifacts, and perform unloads from Amazon Redshift. For example, the mybucket/extract_rs_logs/data bucket is used for storing all the exported query logs for each system table partitioned by the cluster. The mybucket/extract_rs_logs/temp/ bucket is used for temporarily staging the unloaded data from Amazon Redshift. The mybucket/extract_rs_logs/code bucket is used for storing all the code artifacts required for Lambda and the AWS Glue ETL jobs.
ExportEnabledRedshiftClusters Requires Input A comma-separated list of cluster names from which the system table logs need to be exported.
DataStoreSecurityGroups Requires Input A list of security groups with an inbound rule to the Amazon Redshift clusters provided in the parameter, ExportEnabledClusters. These security groups should also have a self-referencing inbound rule on all TCP ports, as explained on Connecting to a JDBC Data Store in a VPC.

After you launch the template and create the stack, you see that the following resources have been created:

  1. AWS Glue connections for each Amazon Redshift cluster you provided in the CloudFormation stack parameter, ExportEnabledRedshiftClusters.
  2. All parameters required for this solution created in the parameter store.
  3. The Lambda function that invokes the AWS Glue ETL jobs for each configured Amazon Redshift cluster at a regular interval of five minutes.
  4. The DynamoDB table that captures the last exported time stamps for each exported cluster-table combination.
  5. The AWS Glue ETL jobs to export query logs from each Amazon Redshift cluster provided in the CloudFormation stack parameter, ExportEnabledRedshiftClusters.
  6. The IAM roles and policies required for the Lambda function and AWS Glue ETL jobs.

After the deployment

For each Amazon Redshift cluster for which you enabled the solution through the CloudFormation stack parameter, ExportEnabledRedshiftClusters, the automated deployment includes temporary credentials that you must update after the deployment:

  1. Go to the parameter store.
  2. Note the parameters <<cluster_name>>.user and redshift_query_logs.<<cluster_name>>.password that correspond to each Amazon Redshift cluster for which you enabled this solution. Edit these parameters to replace the placeholder values with the right credentials.

For example, if product-warehouse is one of the clusters for which you enabled system table export, you edit these two parameters with the right user name and password and choose Save parameter.

Querying the exported system tables

Within a few minutes after the solution deployment, you should see Amazon Redshift query logs being exported to the Amazon S3 location, <<S3Bucket_you_provided>>/extract_redshift_query_logs/data/. In that bucket, you should see the eight system tables partitioned by customer name and date: stl_alert_event_log, stl_dlltext, stl_explain, stl_query, stl_querytext, stl_scan, stl_utilitytext, and stl_wlm_query.

To run cross-cluster diagnostic queries on the exported system tables, create external tables in the AWS Glue Data Catalog. To make it easier for you to get started, I provide a CloudFormation template that creates an AWS Glue crawler, which crawls the exported system tables stored in Amazon S3 and builds the external tables in the AWS Glue Data Catalog.

Launch this CloudFormation template to create external tables that correspond to the Amazon Redshift system tables. S3Bucket is the only input parameter required for this stack deployment. Provide the same Amazon S3 bucket name where the system tables’ data is being exported. After you successfully create the stack, you can see the eight tables in the database, redshift_query_logs_db, as shown in the following screenshot.

Now, navigate to the Athena console to run cross-cluster diagnostic queries. The following screenshot shows a diagnostic query executed in Athena that retrieves query alerts logged across multiple Amazon Redshift clusters.

You can build the following example Amazon QuickSight dashboard by running cross-cluster diagnostic queries on Athena to identify the hourly query count and the key query alert events across multiple Amazon Redshift clusters.

How to extend the solution

You can extend this post’s solution in two ways:

  • Add any new Amazon Redshift clusters that you spin up after you deploy the solution.
  • Add other system tables or custom query results to the list of exports from an Amazon Redshift cluster.

Extend the solution to other Amazon Redshift clusters

To extend the solution to more Amazon Redshift clusters, add the three cluster-specific parameters in the AWS Systems Manager parameter store following the guidelines earlier in this post. Modify the redshift_query_logs.global.enabled_cluster_list parameter to append the new cluster to the comma-separated string.

Extend the solution to add other tables or custom queries to an Amazon Redshift cluster

The current solution ships with the export functionality for the following Amazon Redshift system tables:

  • stl_alert_event_log
  • stl_dlltext
  • stl_explain
  • stl_query
  • stl_querytext
  • stl_scan
  • stl_utilitytext
  • stl_wlm_query

You can easily add another system table or custom query by adding a few lines of code to the AWS Glue ETL job, <<cluster-name>_extract_rs_query_logs. For example, suppose that from the product-warehouse Amazon Redshift cluster you want to export orders greater than $2,000. To do so, add the following five lines of code to the AWS Glue ETL job product-warehouse_extract_rs_query_logs, where product-warehouse is your cluster name:

  1. Get the last-processed time-stamp value. The function creates a value if it doesn’t already exist.

salesLastProcessTSValue = functions.getLastProcessedTSValue(trackingEntry=”mydb.sales_2000",job_configs=job_configs)

  1. Run the custom query with the time stamp.

returnDF=functions.runQuery(query="select * from sales s join order o where o.order_amnt > 2000 and sale_timestamp > '{}'".format (salesLastProcessTSValue) ,tableName="mydb.sales_2000",job_configs=job_configs)

  1. Save the results to Amazon S3.

functions.saveToS3(dataframe=returnDF,s3Prefix=s3Prefix,tableName="mydb.sales_2000",partitionColumns=["sale_date"],job_configs=job_configs)

  1. Get the latest time-stamp value from the returned data frame in Step 2.

latestTimestampVal=functions.getMaxValue(returnDF,"sale_timestamp",job_configs)

  1. Update the last-processed time-stamp value in the DynamoDB table.

functions.updateLastProcessedTSValue(“mydb.sales_2000",latestTimestampVal[0],job_configs)

Conclusion

In this post, I demonstrate a serverless solution to retain the system tables’ log data across multiple Amazon Redshift clusters. By using this solution, you can incrementally export the data from system tables into Amazon S3. By performing this export, you can build cross-cluster diagnostic queries, build audit dashboards, and derive insights into capacity planning by using services such as Athena. I also demonstrate how you can extend this solution to other ad hoc query use cases or tables other than system tables by adding a few lines of code.


Additional Reading

If you found this post useful, be sure to check out Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production and Amazon Redshift – 2017 Recap.


About the Author

Karthik Sonti is a senior big data architect at Amazon Web Services. He helps AWS customers build big data and analytical solutions and provides guidance on architecture and best practices.

 

 

 

 

MPA Reveals Scale of Worldwide Pirate Site Blocking

Post Syndicated from Andy original https://torrentfreak.com/mpa-reveals-scale-of-worldwide-pirate-site-blocking-180410/

Few people following the controversial topic of Internet piracy will be unaware of the site-blocking phenomenon. It’s now one of the main weapons in the entertainment industries’ arsenal and it’s affecting dozens of countries.

While general figures can be culled from the hundreds of news reports covering the issue, the manner in which blocking is handled in several regions means that updates aren’t always provided. New sites are regularly added to blocklists without fanfare, meaning that the public is kept largely in the dark.

Now, however, a submission to the Canadian Radio-television and Telecommunications Commission (CRTC) by Motion Picture Association Canada provides a more detailed overview. It was presented in support of the proposed blocking regime in Canada, so while the key figures are no doubt accurate, some of the supporting rhetoric should be viewed in context.

“Over the last decade, at least 42 countries have either adopted and implemented, or are legally obligated to adopt and implement, measures to ensure that ISPs take steps to disable access to copyright infringing websites, including throughout the European Union, the United Kingdom, Australia, and South Korea,” the submission reads.

The 42 blocking-capable countries referenced by the Hollywood group include the members of the European Union plus the following: Argentina, Australia, Iceland, India, Israel, Liechtenstein, Malaysia, Mexico, Norway, Russia, Singapore, South Korea, and Thailand.

While all countries have their own unique sets of legislation, countries within the EU are covered by the requirements of Article 8.3 of the INFOSEC Directive which provides that; “Member States shall ensure that rightholders are in a position to apply for an injunction against intermediaries whose services are used by a third party to infringe a copyright or related right.”

That doesn’t mean that all countries are actively blocking, however. While Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Poland, Romania, Slovakia, and Slovenia have the legal basis to block infringing sites, none have yet done so.

In a significant number of other EU countries, however, blocking activity is prolific.

“To date, in at least 17 European countries, over 1,800 infringing sites and over 5,300 domains utilized by such sites have been blocked, including in the following four countries where the positive impact of site-blocking over time has been demonstrated,” MPA Canada notes.

Major blocking nations in the EU

At this point, it’s worth pointing out that authority to block sites is currently being obtained in two key ways, either through the courts or via an administrative process.

In the examples above, the UK and Denmark are dealt with via the former, with Italy and Portugal handled via the latter. At least as far as the volume of sites is concerned, court processes – which can be expensive – tend to yield lower site blocking levels than those carried out through an administrative process. Indeed, the MPAA has praised Portugal’s super-streamlined efforts as something to aspire to.

Outside Europe, the same two processes are also in use. For example, Australia, Argentina, and Singapore utilize the judicial route while South Korea, Mexico, Malaysia and Indonesia have opted for administrative remedies.

“Across 10 of these countries, over 1,100 infringing sites and over 1,500 domains utilized by such sites have been blocked,” MPA Canada reveals.

To date, South Korea has blocked 460 sites and 547 domains, while Australia has blocked 91 sites and 355 domains. In the case of the latter, “research has confirmed the increasingly positive impact that site-blocking has, as a greater number of sites are blocked over time,” the Hollywood group notes.

Although by no means comprehensive, MPA Canada lists the following “Notorious Sites” as subject to blocking in multiple countries via both judicial and administrative means. Most will be familiar, with the truly notorious The Pirate Bay heading the pile. Several no longer exist in their original form but in many cases, clones are blocked as if they still represent the original target.


The methods used to block the sites vary from country to country, dependent on what courts deem fit and in consideration of ISPs’ technical capabilities. Three main tools are in use including DNS blocking, IP address blocking, and URL blocking, which can also include Deep Packet Inspection.

The MPA submission (pdf) is strongly in favor of adding Canada to the list of site-blocking countries detailed above. The Hollywood group believes that the measures are both effective and proportionate, citing reduced usage of blocked sites, reduced traffic to pirate sites in general, and increased visits to legitimate platforms.

“There is every reason to believe that the website blocking measures [presented to the CRTC] will lead to the same beneficial results in Canada,” MPA Canada states.

While plenty of content creators and distributors are in favor of proposals, all signs suggest they will have a battle on their hands, with even some ISPs coming out in opposition.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN reviews, discounts, offers and coupons.

Rotate Amazon RDS database credentials automatically with AWS Secrets Manager

Post Syndicated from Apurv Awasthi original https://aws.amazon.com/blogs/security/rotate-amazon-rds-database-credentials-automatically-with-aws-secrets-manager/

Recently, we launched AWS Secrets Manager, a service that makes it easier to rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. You can configure Secrets Manager to rotate secrets automatically, which can help you meet your security and compliance needs. Secrets Manager offers built-in integrations for MySQL, PostgreSQL, and Amazon Aurora on Amazon RDS, and can rotate credentials for these databases natively. You can control access to your secrets by using fine-grained AWS Identity and Access Management (IAM) policies. To retrieve secrets, employees replace plaintext secrets with a call to Secrets Manager APIs, eliminating the need to hard-code secrets in source code or update configuration files and redeploy code when secrets are rotated.

In this post, I introduce the key features of Secrets Manager. I then show you how to store a database credential for a MySQL database hosted on Amazon RDS and how your applications can access this secret. Finally, I show you how to configure Secrets Manager to rotate this secret automatically.

Key features of Secrets Manager

These features include the ability to:

  • Rotate secrets safely. You can configure Secrets Manager to rotate secrets automatically without disrupting your applications. Secrets Manager offers built-in integrations for rotating credentials for Amazon RDS databases for MySQL, PostgreSQL, and Amazon Aurora. You can extend Secrets Manager to meet your custom rotation requirements by creating an AWS Lambda function to rotate other types of secrets. For example, you can create an AWS Lambda function to rotate OAuth tokens used in a mobile application. Users and applications retrieve the secret from Secrets Manager, eliminating the need to email secrets to developers or update and redeploy applications after AWS Secrets Manager rotates a secret.
  • Secure and manage secrets centrally. You can store, view, and manage all your secrets. By default, Secrets Manager encrypts these secrets with encryption keys that you own and control. Using fine-grained IAM policies, you can control access to secrets. For example, you can require developers to provide a second factor of authentication when they attempt to retrieve a production database credential. You can also tag secrets to help you discover, organize, and control access to secrets used throughout your organization.
  • Monitor and audit easily. Secrets Manager integrates with AWS logging and monitoring services to enable you to meet your security and compliance requirements. For example, you can audit AWS CloudTrail logs to see when Secrets Manager rotated a secret or configure AWS CloudWatch Events to alert you when an administrator deletes a secret.
  • Pay as you go. Pay for the secrets you store in Secrets Manager and for the use of these secrets; there are no long-term contracts or licensing fees.

Get started with Secrets Manager

Now that you’re familiar with the key features, I’ll show you how to store the credential for a MySQL database hosted on Amazon RDS. To demonstrate how to retrieve and use the secret, I use a python application running on Amazon EC2 that requires this database credential to access the MySQL instance. Finally, I show how to configure Secrets Manager to rotate this database credential automatically. Let’s get started.

Phase 1: Store a secret in Secrets Manager

  1. Open the Secrets Manager console and select Store a new secret.
     
    Secrets Manager console interface
     
  2. I select Credentials for RDS database because I’m storing credentials for a MySQL database hosted on Amazon RDS. For this example, I store the credentials for the database superuser. I start by securing the superuser because it’s the most powerful database credential and has full access over the database.
     
    Store a new secret interface with Credentials for RDS database selected
     

    Note: For this example, you need permissions to store secrets in Secrets Manager. To grant these permissions, you can use the AWSSecretsManagerReadWriteAccess managed policy. Read the AWS Secrets Manager Documentation for more information about the minimum IAM permissions required to store a secret.

  3. Next, I review the encryption setting and choose to use the default encryption settings. Secrets Manager will encrypt this secret using the Secrets Manager DefaultEncryptionKeyDefaultEncryptionKey in this account. Alternatively, I can choose to encrypt using a customer master key (CMK) that I have stored in AWS KMS.
     
    Select the encryption key interface
     
  4. Next, I view the list of Amazon RDS instances in my account and select the database this credential accesses. For this example, I select the DB instance mysql-rds-database, and then I select Next.
     
    Select the RDS database interface
     
  5. In this step, I specify values for Secret Name and Description. For this example, I use Applications/MyApp/MySQL-RDS-Database as the name and enter a description of this secret, and then select Next.
     
    Secret Name and description interface
     
  6. For the next step, I keep the default setting Disable automatic rotation because my secret is used by my application running on Amazon EC2. I’ll enable rotation after I’ve updated my application (see Phase 2 below) to use Secrets Manager APIs to retrieve secrets. I then select Next.

    Note: If you’re storing a secret that you’re not using in your application, select Enable automatic rotation. See our AWS Secrets Manager getting started guide on rotation for details.

     
    Configure automatic rotation interface
     

  7. Review the information on the next screen and, if everything looks correct, select Store. We’ve now successfully stored a secret in Secrets Manager.
  8. Next, I select See sample code.
     
    The See sample code button
     
  9. Take note of the code samples provided. I will use this code to update my application to retrieve the secret using Secrets Manager APIs.
     
    Python sample code
     

Phase 2: Update an application to retrieve secret from Secrets Manager

Now that I have stored the secret in Secrets Manager, I update my application to retrieve the database credential from Secrets Manager instead of hard coding this information in a configuration file or source code. For this example, I show how to configure a python application to retrieve this secret from Secrets Manager.

  1. I connect to my Amazon EC2 instance via Secure Shell (SSH).
  2. Previously, I configured my application to retrieve the database user name and password from the configuration file. Below is the source code for my application.
    import MySQLdb
    import config

    def no_secrets_manager_sample()

    # Get the user name, password, and database connection information from a config file.
    database = config.database
    user_name = config.user_name
    password = config.password

    # Use the user name, password, and database connection information to connect to the database
    db = MySQLdb.connect(database.endpoint, user_name, password, database.db_name, database.port)

  3. I use the sample code from Phase 1 above and update my application to retrieve the user name and password from Secrets Manager. This code sets up the client and retrieves and decrypts the secret Applications/MyApp/MySQL-RDS-Database. I’ve added comments to the code to make the code easier to understand.
    # Use the code snippet provided by Secrets Manager.
    import boto3
    from botocore.exceptions import ClientError

    def get_secret():
    #Define the secret you want to retrieve
    secret_name = "Applications/MyApp/MySQL-RDS-Database"
    #Define the Secrets mManager end-point your code should use.
    endpoint_url = "https://secretsmanager.us-east-1.amazonaws.com"
    region_name = "us-east-1"

    #Setup the client
    session = boto3.session.Session()
    client = session.client(
    service_name='secretsmanager',
    region_name=region_name,
    endpoint_url=endpoint_url
    )

    #Use the client to retrieve the secret
    try:
    get_secret_value_response = client.get_secret_value(
    SecretId=secret_name
    )
    #Error handling to make it easier for your code to tolerate faults
    except ClientError as e:
    if e.response['Error']['Code'] == 'ResourceNotFoundException':
    print("The requested secret " + secret_name + " was not found")
    elif e.response['Error']['Code'] == 'InvalidRequestException':
    print("The request was invalid due to:", e)
    elif e.response['Error']['Code'] == 'InvalidParameterException':
    print("The request had invalid params:", e)
    else:
    # Decrypted secret using the associated KMS CMK
    # Depending on whether the secret was a string or binary, one of these fields will be populated
    if 'SecretString' in get_secret_value_response:
    secret = get_secret_value_response['SecretString']
    else:
    binary_secret_data = get_secret_value_response['SecretBinary']

    # Your code goes here.

  4. Applications require permissions to access Secrets Manager. My application runs on Amazon EC2 and uses an IAM role to obtain access to AWS services. I will attach the following policy to my IAM role. This policy uses the GetSecretValue action to grant my application permissions to read secret from Secrets Manager. This policy also uses the resource element to limit my application to read only the Applications/MyApp/MySQL-RDS-Database secret from Secrets Manager. You can visit the AWS Secrets Manager Documentation to understand the minimum IAM permissions required to retrieve a secret.
    {
    "Version": "2012-10-17",
    "Statement": {
    "Sid": "RetrieveDbCredentialFromSecretsManager",
    "Effect": "Allow",
    "Action": "secretsmanager:GetSecretValue",
    "Resource": "arn:aws:secretsmanager:::secret:Applications/MyApp/MySQL-RDS-Database"
    }
    }

Phase 3: Enable Rotation for Your Secret

Rotating secrets periodically is a security best practice because it reduces the risk of misuse of secrets. Secrets Manager makes it easy to follow this security best practice and offers built-in integrations for rotating credentials for MySQL, PostgreSQL, and Amazon Aurora databases hosted on Amazon RDS. When you enable rotation, Secrets Manager creates a Lambda function and attaches an IAM role to this function to execute rotations on a schedule you define.

Note: Configuring rotation is a privileged action that requires several IAM permissions and you should only grant this access to trusted individuals. To grant these permissions, you can use the AWS IAMFullAccess managed policy.

Next, I show you how to configure Secrets Manager to rotate the secret Applications/MyApp/MySQL-RDS-Database automatically.

  1. From the Secrets Manager console, I go to the list of secrets and choose the secret I created in the first step Applications/MyApp/MySQL-RDS-Database.
     
    List of secrets in the Secrets Manager console
     
  2. I scroll to Rotation configuration, and then select Edit rotation.
     
    Rotation configuration interface
     
  3. To enable rotation, I select Enable automatic rotation. I then choose how frequently I want Secrets Manager to rotate this secret. For this example, I set the rotation interval to 60 days.
     
    Edit rotation configuration interface
     
  4. Next, Secrets Manager requires permissions to rotate this secret on your behalf. Because I’m storing the superuser database credential, Secrets Manager can use this credential to perform rotations. Therefore, I select Use the secret that I provided in step 1, and then select Next.
     
    Select which secret to use in the Edit rotation configuration interface
     
  5. The banner on the next screen confirms that I have successfully configured rotation and the first rotation is in progress, which enables you to verify that rotation is functioning as expected. Secrets Manager will rotate this credential automatically every 60 days.
     
    Confirmation banner message
     

Summary

I introduced AWS Secrets Manager, explained the key benefits, and showed you how to help meet your compliance requirements by configuring AWS Secrets Manager to rotate database credentials automatically on your behalf. Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own secrets management infrastructure. To get started, visit the Secrets Manager console. To learn more, visit Secrets Manager documentation.

If you have comments about this post, submit them in the Comments section below. If you have questions about anything in this post, start a new thread on the Secrets Manager forum.

Want more AWS Security news? Follow us on Twitter.

Amazon Transcribe Now Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-transcribe-now-generally-available/


At AWS re:Invent 2017 we launched Amazon Transcribe in private preview. Today we’re excited to make Amazon Transcribe generally available for all developers. Amazon Transcribe is an automatic speech recognition service (ASR) that makes it easy for developers to add speech to text capabilities to their applications. We’ve iterated on customer feedback in the preview to make a number of enhancements to Amazon Transcribe.

New Amazon Transcribe Features in GA

To start off we’ve made the SampleRate parameter optional which means you only need to know the file type of your media and the input language. We’ve added two new features – the ability to differentiate multiple speakers in the audio to provide more intelligible transcripts (“who spoke when”), and a custom vocabulary to improve the accuracy of speech recognition for product names, industry-specific terminology, or names of individuals. To refresh our memories on how Amazon Transcribe works lets look at a quick example. I’ll convert this audio in my S3 bucket.

import boto3
transcribe = boto3.client("transcribe")
transcribe.start_transcription_job(
    TranscriptionJobName="TranscribeDemo",
    LanguageCode="en-US",
    MediaFormat="mp3",
    Media={"MediaFileUri": "https://s3.amazonaws.com/randhunt-transcribe-demo-us-east-1/out.mp3"}
)

This will output JSON similar to this (I’ve stripped out most of the response) with indidivudal speakers identified:

{
  "jobName": "reinvent",
  "accountId": "1234",
  "results": {
    "transcripts": [
      {
        "transcript": "Hi, everybody, i'm randall ..."
      }
    ],
    "speaker_labels": {
      "speakers": 2,
      "segments": [
        {
          "start_time": "0.000000",
          "speaker_label": "spk_0",
          "end_time": "0.010",
          "items": []
        },
        {
          "start_time": "0.010000",
          "speaker_label": "spk_1",
          "end_time": "4.990",
          "items": [
            {
              "start_time": "1.000",
              "speaker_label": "spk_1",
              "end_time": "1.190"
            },
            {
              "start_time": "1.190",
              "speaker_label": "spk_1",
              "end_time": "1.700"
            }
          ]
        }
      ]
    },
    "items": [
      {
        "start_time": "1.000",
        "end_time": "1.190",
        "alternatives": [
          {
            "confidence": "0.9971",
            "content": "Hi"
          }
        ],
        "type": "pronunciation"
      },
      {
        "alternatives": [
          {
            "content": ","
          }
        ],
        "type": "punctuation"
      },
      {
        "start_time": "1.190",
        "end_time": "1.700",
        "alternatives": [
          {
            "confidence": "1.0000",
            "content": "everybody"
          }
        ],
        "type": "pronunciation"
      }
    ]
  },
  "status": "COMPLETED"
}

Custom Vocabulary

Now if I needed to have a more complex technical discussion with a colleague I could create a custom vocabulary. A custom vocabulary is specified as an array of strings passed to the CreateVocabulary API and you can include your custom vocabulary in a transcription job by passing in the name as part of the Settings in a StartTranscriptionJob API call. An individual vocabulary can be as large as 50KB and each phrase must be less than 256 characters. If I wanted to transcribe the recordings of my highschool AP Biology class I could create a custom vocabulary in Python like this:

import boto3
transcribe = boto3.client("transcribe")
transcribe.create_vocabulary(
LanguageCode="en-US",
VocabularyName="APBiology"
Phrases=[
    "endoplasmic-reticulum",
    "organelle",
    "cisternae",
    "eukaryotic",
    "ribosomes",
    "hepatocyes",
    "cell-membrane"
]
)

I can refer to this vocabulary later on by the name APBiology and update it programatically based on any errors I may find in the transcriptions.

Available Now

Amazon Transcribe is available now in US East (N. Virginia), US West (Oregon), US East (Ohio) and EU (Ireland). Transcribe’s free tier gives you 60 minutes of transcription for free per month for the first 12 months with a pay-as-you-go model of $0.0004 per second of transcribed audio after that, with a minimum charge of 15 seconds.

When combined with other tools and services I think transcribe opens up a entirely new opportunities for application development. I’m excited to see what technologies developers build with this new service.

Randall

Backblaze Announces B2 Compute Partnerships

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/introducing-cloud-compute-services/

Backblaze Announces B2 Compute Partnerships

In 2015, we announced Backblaze B2 Cloud Storage — the most affordable, high performance storage cloud on the planet. The decision to release B2 as a service was in direct response to customers asking us if they could use the same cloud storage infrastructure we use for our Computer Backup service. With B2, we entered a market in direct competition with Amazon S3, Google Cloud Services, and Microsoft Azure Storage. Today, we have over 500 petabytes of data from customers in over 150 countries. At $0.005 / GB / month for storage (1/4th of S3) and $0.01 / GB for downloads (1/5th of S3), it turns out there’s a healthy market for cloud storage that’s easy and affordable.

As B2 has grown, customers wanted to use our cloud storage for a variety of use cases that required not only storage but compute. We’re happy to say that through partnerships with Packet & ServerCentral, today we’re announcing that compute is now available for B2 customers.

Cloud Compute and Storage

Backblaze has directly connected B2 with the compute servers of Packet and ServerCentral, thereby allowing near-instant (< 10 ms) data transfers between services. Also, transferring data between B2 and both our compute partners is free.

  • Storing data in B2 and want to run an AI analysis on it? — There are no fees to move the data to our compute partners.
  • Generating data in an application? — Run the application with one of our partners and store it in B2.
  • Transfers are free and you’ll save more than 50% off of the equivalent set of services from AWS.

These partnerships enable B2 customers to use compute, give our compute partners’ customers access to cloud storage, and introduce new customers to industry-leading storage and compute — all with high-performance, low-latency, and low-cost.

Is This a Big Deal? We Think So

Compute is one of the most requested services from our customers Why? Because it unlocks a number of use cases for them. Let’s look at three popular examples:

Transcoding Media Files

B2 has earned wide adoption in the Media & Entertainment (“M&E”) industry. Our affordable storage and download pricing make B2 great for a wide variety of M&E use cases. But many M&E workflows require compute. Content syndicators, like American Public Television, need the ability to transcode files to meet localization and distribution management requirements.

There are a multitude of reasons that transcode is needed — thumbnail and proxy generation enable M&E professionals to work efficiently. Without compute, the act of transcoding files remains cumbersome. Either the files need to be brought down from the cloud, transcoded, and then pushed back up or they must be kept locally until the project is complete. Both scenarios are inefficient.

Starting today, any content producer can spin up compute with one of our partners, pay by the hour for their transcode processing, and return the new media files to B2 for storage and distribution. The company saves money, moves faster, and ensures their files are safe and secure.

Disaster Recovery

Backblaze’s heritage is based on providing outstanding backup services. When you have incredibly affordable cloud storage, it ends up being a great destination for your backup data.

Most enterprises have virtual machines (“VMs”) running in their infrastructure and those VMs need to be backed up. In a disaster scenario, a business wants to know they can get back up and running quickly.

With all data stored in B2, a business can get up and running quickly. Simply restore your backed up VM to one of our compute providers, and your business will be able to get back online.

Since B2 does not place restrictions, delays, or penalties on getting data out, customers can get back up and running quickly and affordably.

Saving $74 Million (aka “The Dropbox Effect”)

Ten years ago, Backblaze decided that S3 was too costly a platform to build its cloud storage business. Instead, we created the Backblaze Storage Pod and our own cloud storage infrastructure. That decision enabled us to offer our customers storage at a previously unavailable price point and maintain those prices for over a decade. It also laid the foundation for Netflix Open Connect and Facebook Open Compute.

Dropbox recently migrated the majority of their cloud services off of AWS and onto Dropbox’s own infrastructure. By leaving AWS, Dropbox was able to build out their own data centers and still save over $74 Million. They achieved those savings by avoiding the fees AWS charges for storing and downloading data, which, incidentally, are five times higher than Backblaze B2.

For Dropbox, being able to realize savings was possible because they have access to enough capital and expertise that they can build out their own infrastructure. For companies that have such resources and scale, that’s a great answer.

“Before this offering, the economics of the cloud would have made our business simply unviable.” — Gabriel Menegatti, SlicingDice

The questions Backblaze and our compute partners pondered was “how can we democratize the Dropbox effect for our storage and compute customers? How can we help customers do more and pay less?” The answer we came up with was to connect Backblaze’s B2 storage with strategic compute partners and remove any transfer fees between them. You may not save $74 million as Dropbox did, but you can choose the optimal providers for your use case and realize significant savings in the process.

This Sounds Good — Tell Me More About Your Partners

We’re very fortunate to be launching our compute program with two fantastic partners in Packet and ServerCentral. These partners allow us to offer a range of computing services.

Packet

We recommend Packet for customers that need on-demand, high performance, bare metal servers available by the hour. They also have robust offerings for private / customized deployments. Their offerings end up costing 50-75% of the equivalent offerings from EC2.

To get started with Packet and B2, visit our partner page on Packet.net.

ServerCentral

ServerCentral is the right partner for customers that have business and IT challenges that require more than “just” hardware. They specialize in fully managed, custom cloud solutions that solve complex business and IT challenges. ServerCentral also has expertise in managed network solutions to address global connectivity and content delivery.

To get started with ServerCentral and B2, visit our partner page on ServerCentral.com.

What’s Next?

We’re excited to find out. The combination of B2 and compute unlocks use cases that were previously impossible or at least unaffordable.

“The combination of performance and price offered by this partnership enables me to create an entirely new business line. Before this offering, the economics of the cloud would have made our business simply unviable,” noted Gabriel Menegatti, co-founder at SlicingDice, a serverless data warehousing service. “Knowing that transfers between compute and B2 are free means I don’t have to worry about my business being successful. And, with download pricing from B2 at just $0.01 GB, I know I’m avoiding a 400% tax from AWS on data I retrieve.”

What can you do with B2 & compute? Please share your ideas with us in the comments. And, for those attending NAB 2018 in Las Vegas next week, please come by and say hello!

The post Backblaze Announces B2 Compute Partnerships appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.