Tag Archives: Fun

Streaming Service iflix Buys Shows Based on Piracy Data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Announcing the Winners of the AWS Chatbot Challenge – Conversational, Intelligent Chatbots using Amazon Lex and AWS Lambda

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/announcing-the-winners-of-the-aws-chatbot-challenge-conversational-intelligent-chatbots-using-amazon-lex-and-aws-lambda/

A couple of months ago on the blog, I announced the AWS Chatbot Challenge in conjunction with Slack. The AWS Chatbot Challenge was an opportunity to build a unique chatbot that helped to solve a problem or that would add value for its prospective users. The mission was to build a conversational, natural language chatbot using Amazon Lex and leverage Lex’s integration with AWS Lambda to execute logic or data processing on the backend.

I know that you all have been anxiously waiting to hear announcements of who were the winners of the AWS Chatbot Challenge as much as I was. Well wait no longer, the winners of the AWS Chatbot Challenge have been decided.

May I have the Envelope Please? (The Trumpets sound)

The winners of the AWS Chatbot Challenge are:

  • First Place: BuildFax Counts by Joe Emison
  • Second Place: Hubsy by Andrew Riess, Andrew Puch, and John Wetzel
  • Third Place: PFMBot by Benny Leong and his team from MoneyLion.
  • Large Organization Winner: ADP Payroll Innovation Bot by Eric Liu, Jiaxing Yan, and Fan Yang

 

Diving into the Winning Chatbot Projects

Let’s take a walkthrough of the details for each of the winning projects to get a view of what made these chatbots distinctive, as well as, learn more about the technologies used to implement the chatbot solution.

 

BuildFax Counts by Joe Emison

The BuildFax Counts bot was created as a real solution for the BuildFax company to decrease the amount the time that sales and marketing teams can get answers on permits or properties with permits meet certain criteria.

BuildFax, a company co-founded by bot developer Joe Emison, has the only national database of building permits, which updates data from approximately half of the United States on a monthly basis. In order to accommodate the many requests that come in from the sales and marketing team regarding permit information, BuildFax has a technical sales support team that fulfills these requests sent to a ticketing system by manually writing SQL queries that run across the shards of the BuildFax databases. Since there are a large number of requests received by the internal sales support team and due to the manual nature of setting up the queries, it may take several days for getting the sales and marketing teams to receive an answer.

The BuildFax Counts chatbot solves this problem by taking the permit inquiry that would normally be sent into a ticket from the sales and marketing team, as input from Slack to the chatbot. Once the inquiry is submitted into Slack, a query executes and the inquiry results are returned immediately.

Joe built this solution by first creating a nightly export of the data in their BuildFax MySQL RDS database to CSV files that are stored in Amazon S3. From the exported CSV files, an Amazon Athena table was created in order to run quick and efficient queries on the data. He then used Amazon Lex to create a bot to handle the common questions and criteria that may be asked by the sales and marketing teams when seeking data from the BuildFax database by modeling the language used from the BuildFax ticketing system. He added several different sample utterances and slot types; both custom and Lex provided, in order to correctly parse every question and criteria combination that could be received from an inquiry.  Using Lambda, Joe created a Javascript Lambda function that receives information from the Lex intent and used it to build a SQL statement that runs against the aforementioned Athena database using the AWS SDK for JavaScript in Node.js library to return inquiry count result and SQL statement used.

The BuildFax Counts bot is used today for the BuildFax sales and marketing team to get back data on inquiries immediately that previously took up to a week to receive results.

Not only is BuildFax Counts bot our 1st place winner and wonderful solution, but its creator, Joe Emison, is a great guy.  Joe has opted to donate his prize; the $5,000 cash, the $2,500 in AWS Credits, and one re:Invent ticket to the Black Girls Code organization. I must say, you rock Joe for helping these kids get access and exposure to technology.

 

Hubsy by Andrew Riess, Andrew Puch, and John Wetzel

Hubsy bot was created to redefine and personalize the way users traditionally manage their HubSpot account. HubSpot is a SaaS system providing marketing, sales, and CRM software. Hubsy allows users of HubSpot to create engagements and log engagements with customers, provide sales teams with deals status, and retrieves client contact information quickly. Hubsy uses Amazon Lex’s conversational interface to execute commands from the HubSpot API so that users can gain insights, store and retrieve data, and manage tasks directly from Facebook, Slack, or Alexa.

In order to implement the Hubsy chatbot, Andrew and the team members used AWS Lambda to create a Lambda function with Node.js to parse the users request and call the HubSpot API, which will fulfill the initial request or return back to the user asking for more information. Terraform was used to automatically setup and update Lambda, CloudWatch logs, as well as, IAM profiles. Amazon Lex was used to build the conversational piece of the bot, which creates the utterances that a person on a sales team would likely say when seeking information from HubSpot. To integrate with Alexa, the Amazon Alexa skill builder was used to create an Alexa skill which was tested on an Echo Dot. Cloudwatch Logs are used to log the Lambda function information to CloudWatch in order to debug different parts of the Lex intents. In order to validate the code before the Terraform deployment, ESLint was additionally used to ensure the code was linted and proper development standards were followed.

 

PFMBot by Benny Leong and his team from MoneyLion

PFMBot, Personal Finance Management Bot,  is a bot to be used with the MoneyLion finance group which offers customers online financial products; loans, credit monitoring, and free credit score service to improve the financial health of their customers. Once a user signs up an account on the MoneyLion app or website, the user has the option to link their bank accounts with the MoneyLion APIs. Once the bank account is linked to the APIs, the user will be able to login to their MoneyLion account and start having a conversation with the PFMBot based on their bank account information.

The PFMBot UI has a web interface built with using Javascript integration. The chatbot was created using Amazon Lex to build utterances based on the possible inquiries about the user’s MoneyLion bank account. PFMBot uses the Lex built-in AMAZON slots and parsed and converted the values from the built-in slots to pass to AWS Lambda. The AWS Lambda functions interacting with Amazon Lex are Java-based Lambda functions which call the MoneyLion Java-based internal APIs running on Spring Boot. These APIs obtain account data and related bank account information from the MoneyLion MySQL Database.

 

ADP Payroll Innovation Bot by Eric Liu, Jiaxing Yan, and Fan Yang

ADP PI (Payroll Innovation) bot is designed to help employees of ADP customers easily review their own payroll details and compare different payroll data by just asking the bot for results. The ADP PI Bot additionally offers issue reporting functionality for employees to report payroll issues and aids HR managers in quickly receiving and organizing any reported payroll issues.

The ADP Payroll Innovation bot is an ecosystem for the ADP payroll consisting of two chatbots, which includes ADP PI Bot for external clients (employees and HR managers), and ADP PI DevOps Bot for internal ADP DevOps team.


The architecture for the ADP PI DevOps bot is different architecture from the ADP PI bot shown above as it is deployed internally to ADP. The ADP PI DevOps bot allows input from both Slack and Alexa. When input comes into Slack, Slack sends the request to Lex for it to process the utterance. Lex then calls the Lambda backend, which obtains ADP data sitting in the ADP VPC running within an Amazon VPC. When input comes in from Alexa, a Lambda function is called that also obtains data from the ADP VPC running on AWS.

The architecture for the ADP PI bot consists of users entering in requests and/or entering issues via Slack. When requests/issues are entered via Slack, the Slack APIs communicate via Amazon API Gateway to AWS Lambda. The Lambda function either writes data into one of the Amazon DynamoDB databases for recording issues and/or sending issues or it sends the request to Lex. When sending issues, DynamoDB integrates with Trello to keep HR Managers abreast of the escalated issues. Once the request data is sent from Lambda to Lex, Lex processes the utterance and calls another Lambda function that integrates with the ADP API and it calls ADP data from within the ADP VPC, which runs on Amazon Virtual Private Cloud (VPC).

Python and Node.js were the chosen languages for the development of the bots.

The ADP PI bot ecosystem has the following functional groupings:

Employee Functionality

  • Summarize Payrolls
  • Compare Payrolls
  • Escalate Issues
  • Evolve PI Bot

HR Manager Functionality

  • Bot Management
  • Audit and Feedback

DevOps Functionality

  • Reduce call volume in service centers (ADP PI Bot).
  • Track issues and generate reports (ADP PI Bot).
  • Monitor jobs for various environment (ADP PI DevOps Bot)
  • View job dashboards (ADP PI DevOps Bot)
  • Query job details (ADP PI DevOps Bot)

 

Summary

Let’s all wish all the winners of the AWS Chatbot Challenge hearty congratulations on their excellent projects.

You can review more details on the winning projects, as well as, all of the submissions to the AWS Chatbot Challenge at: https://awschatbot2017.devpost.com/submissions. If you are curious on the details of Chatbot challenge contest including resources, rules, prizes, and judges, you can review the original challenge website here:  https://awschatbot2017.devpost.com/.

Hopefully, you are just as inspired as I am to build your own chatbot using Lex and Lambda. For more information, take a look at the Amazon Lex developer guide or the AWS AI blog on Building Better Bots Using Amazon Lex (Part 1)

Chat with you soon!

Tara

Announcement: IPS code

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/08/announcement-ips-code.html

So after 20 years, IBM is killing off my BlackICE code created in April 1998. So it’s time that I rewrite it.

BlackICE was the first “inline” intrusion-detection system, aka. an “intrusion prevention system” or IPS. ISS purchased my company in 2001 and replaced their RealSecure engine with it, and later renamed it Proventia. Then IBM purchased ISS in 2006. Now, they are formally canceling the project and moving customers onto Cisco’s products, which are based on Snort.

So now is a good time to write a replacement. The reason is that BlackICE worked fundamentally differently than Snort, using protocol analysis rather than pattern-matching. In this way, it worked more like Bro than Snort. The biggest benefit of protocol-analysis is speed, making it many times faster than Snort. The second benefit is better detection ability, as I describe in this post on Heartbleed.

So my plan is to create a new project. I’ll be checking in the starter bits into GitHub starting a couple weeks from now. I need to figure out a new name for the project, so I don’t have to rip off a name from William Gibson like I did last time :).

Some notes:

  • Yes, it’ll be GNU open source. I’m a capitalist, so I’ll earn money like snort/nmap dual-licensing it, charging companies who don’t want to open-source their addons. All capitalists GNU license their code.
  • C, not Rust. Sorry, I’m going for extreme scalability. We’ll re-visit this decision later when looking at building protocol parsers.
  • It’ll be 95% compatible with Snort signatures. Their language definition leaves so much ambiguous it’ll be hard to be 100% compatible.
  • It’ll support Snort output as well, though really, Snort’s events suck.
  • Protocol parsers in Lua, so you can use it as a replacement for Bro, writing parsers to extract data you are interested in.
  • Protocol state machine parsers in C, like you see in my Masscan project for X.509.
  • First version IDS only. These days, “inline” means also being able to MitM the SSL stack, so I’m gong to have to think harder on that.
  • Mutli-core worker threads off PF_RING/DPDK/netmap receive queues. Should handle 10gbps, tracking 10 million concurrent connections, with quad-core CPU.
So if you want to contribute to the project, here’s what I need:
  • Requirements from people who work daily with IDS/IPS today. I need you to write up what your products do well that you really like. I need to you write up what they suck at that needs to be fixed. These need to be in some detail.
  • Testing environment to play with. This means having a small server plugged into a real-world link running at a minimum of several gigabits-per-second available for the next year. I’ll sign NDAs related to the data I might see on the network.
  • Coders. I’ll be doing the basic architecture, but protocol parsers, output plugins, etc. will need work. Code will be in C and Lua for the near term. Unfortunately, since I’m going to dual-license, I’ll need waivers before accepting pull requests.
Anyway, follow me on Twitter @erratarob if you want to contribute.

Michael Reeves and the ridiculous Subscriber Robot

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/michael-reeves-subscriber-robot/

At the beginning of his new build’s video, YouTuber Michael Reeves discusses a revelation he had about why some people don’t subscribe to his channel:

The real reason some people don’t subscribe is that when you hit this button, that’s all, that’s it, it’s done. It’s not special, it’s not enjoyable. So how do we make subscribing a fun, enjoyable process? Well, we do it by slowly chipping away at the content creator’s psyche every time someone subscribes.

His fix? The ‘fun’ interactive Subscriber Robot that is the subject of the video.

Be aware that Michael uses a couple of mild swears in this video, so maybe don’t watch it with a child.

The Subscriber Robot

Just showing that subscriber dedication My Patreon Page: https://www.patreon.com/michaelreeves Personal Site: https://michaelreeves.us/ Twitter: https://twitter.com/michaelreeves08 Song: Summer Salt – Sweet To Me

Who is Michael Reeves?

Software developer and student Michael Reeves started his YouTube account a mere four months ago, with the premiere of his robot that shines lasers into your eyes – now he has 110k+ subscribers. At only 19, Michael co-owns and manages a company together with friends, and is set on his career path in software and computing. So when he is not making videos, he works a nine-to-five job “to pay for college and, y’know, live”.

The Subscriber Robot

Michael shot to YouTube fame with the aforementioned laser robot built around an Arduino. But by now he has also be released videos for a few Raspberry Pi-based contraptions.

Michael Reeves Raspberry Pi Subscriber Robot

Michael, talking us through the details of one of the worst ideas ever made

His Subscriber Robot uses a series of Python scripts running on a Raspberry Pi to check for new subscribers to Michael’s channel via the YouTube API. When it identifies one, the Pi uses a relay to make the ceiling lights in Michael’s office flash ten times a second while ear-splitting noise is emitted by a 102-decibel-rated buzzer. Needless to say, this buzzer is not recommended for home use, work use, or any use whatsoever! Moreover, the Raspberry Pi also connects to a speaker that announces the name of the new subscriber, so Michael knows who to thank.

Michael Reeves Raspberry Pi Subscriber Robot

Subscriber Robot: EEH! EEH! EEH! MoistPretzels has subscribed.
Michael: Thank you, MoistPretzels…

Given that Michael has gained a whopping 30,000 followers in the ten days since the release of this video, it’s fair to assume he is currently curled up in a ball on the office floor, quietly crying to himself.

If you think Michael only makes videos about ridiculous builds, you’re mistaken. He also uses YouTube to provide educational content, because he believes that “it’s super important for people to teach themselves how to program”. For example, he has just released a new C# beginners tutorial, the third in the series.

Support Michael

If you’d like to help Michael in his mission to fill the world with both tutorials and ridiculous robot builds, make sure to subscribe to his channel. You can also follow him on Twitter and support him on Patreon.

You may also want to check out the Useless Duck Company and Simone Giertz if you’re in the mood for more impractical, yet highly amusing, robot builds.

Good luck with your channel, Michael! We are looking forward to, and slightly dreading, more videos from one of our favourite new YouTubers.

The post Michael Reeves and the ridiculous Subscriber Robot appeared first on Raspberry Pi.

Unfixable Automobile Computer Security Vulnerability

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

There is an unpatchable vulnerability that affects most modern cars. It’s buried in the Controller Area Network (CAN):

Researchers say this flaw is not a vulnerability in the classic meaning of the word. This is because the flaw is more of a CAN standard design choice that makes it unpatchable.

Patching the issue means changing how the CAN standard works at its lowest levels. Researchers say car manufacturers can only mitigate the vulnerability via specific network countermeasures, but cannot eliminate it entirely.

Details on how the attack works are here:

The CAN messages, including errors, are called “frames.” Our attack focuses on how CAN handles errors. Errors arise when a device reads values that do not correspond to the original expected value on a frame. When a device detects such an event, it writes an error message onto the CAN bus in order to “recall” the errant frame and notify the other devices to entirely ignore the recalled frame. This mishap is very common and is usually due to natural causes, a transient malfunction, or simply by too many systems and modules trying to send frames through the CAN at the same time.

If a device sends out too many errors, then­ — as CAN standards dictate — ­it goes into a so-called Bus Off state, where it is cut off from the CAN and prevented from reading and/or writing any data onto the CAN. This feature is helpful in isolating clearly malfunctioning devices and stops them from triggering the other modules/systems on the CAN.

This is the exact feature that our attack abuses. Our attack triggers this particular feature by inducing enough errors such that a targeted device or system on the CAN is made to go into the Bus Off state, and thus rendered inert/inoperable. This, in turn, can drastically affect the car’s performance to the point that it becomes dangerous and even fatal, especially when essential systems like the airbag system or the antilock braking system are deactivated. All it takes is a specially-crafted attack device, introduced to the car’s CAN through local access, and the reuse of frames already circulating in the CAN rather than injecting new ones (as previous attacks in this manner have done).

Slashdot thread.

timeShift(GrafanaBuzz, 1w) Issue 9

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/08/18/timeshiftgrafanabuzz-1w-issue-9/

Matt from Grafana NYC spent the week visiting Stockholm to focus on v5.0 with Torkel. Despite warnings otherwise, the weather has been beautiful, making a nice backdrop for many UX discussions. Very, very excited to soon show what we’ve been working on.


Latest Release

Grafana v4.4.3 is Available for download

To see the full changelog, head over to our community site.


Grafana <3 Prometheus

Our very own Carl Bergquist spoke at PromCon 2017 yesterday in Munich, highlighting recent Grafana features and enhancements.

We also used the opportunity to debut our coming Prometheus query editor with a load of new functionality; seems the community approves,
in fact this is our most popular tweet ever!


From the Blogosphere

  • Wikimedia Metrics: A tweet this week reminded us of the public metrics Wikimedia exposes using Grafana. Exploring the performance stats in real time for the 5th mot popular site on the internet is pretty fun.

  • Creating Grafana Annotations with InfluxDB: Nice short article by Max Chadwick showing how to quickly add InfluxDB as a source for Grafana annotations.


This week’s MVC (Most Valuable Contributor)

This week’s MVC highlights what is great about Open Source software.

ericslaw
ericslaw submitted his first PR to a public project this past week. Speaking from personal experience, submitting a PR can feel daunting and and we were lucky that he chose Grafana. Even the smallest contributions, like Eric fixing a bogus link within our templating has big impact.


Tweet of the Week

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

Seems the excitement about Prometheus and Grafana has also caught the attention of a certain superhero.



What do you think?

That wraps up another issue. Hope you’re finding these roundups valuable. Let us know how we’re doing! Submit a comment on this article below, or post something at our community forum. Help us make this better!

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

Raspbian Stretch has arrived for Raspberry Pi

Post Syndicated from Simon Long original https://www.raspberrypi.org/blog/raspbian-stretch/

It’s now just under two years since we released the Jessie version of Raspbian. Those of you who know that Debian run their releases on a two-year cycle will therefore have been wondering when we might be releasing the next version, codenamed Stretch. Well, wonder no longer – Raspbian Stretch is available for download today!

Disney Pixar Toy Story Raspbian Stretch Raspberry Pi

Debian releases are named after characters from Disney Pixar’s Toy Story trilogy. In case, like me, you were wondering: Stretch is a purple octopus from Toy Story 3. Hi, Stretch!

The differences between Jessie and Stretch are mostly under-the-hood optimisations, and you really shouldn’t notice any differences in day-to-day use of the desktop and applications. (If you’re really interested, the technical details are in the Debian release notes here.)

However, we’ve made a few small changes to our image that are worth mentioning.

New versions of applications

Version 3.0.1 of Sonic Pi is included – this includes a lot of new functionality in terms of input/output. See the Sonic Pi release notes for more details of exactly what has changed.

Raspbian Stretch Raspberry Pi

The Chromium web browser has been updated to version 60, the most recent stable release. This offers improved memory usage and more efficient code, so you may notice it running slightly faster than before. The visual appearance has also been changed very slightly.

Raspbian Stretch Raspberry Pi

Bluetooth audio

In Jessie, we used PulseAudio to provide support for audio over Bluetooth, but integrating this with the ALSA architecture used for other audio sources was clumsy. For Stretch, we are using the bluez-alsa package to make Bluetooth audio work with ALSA itself. PulseAudio is therefore no longer installed by default, and the volume plugin on the taskbar will no longer start and stop PulseAudio. From a user point of view, everything should still work exactly as before – the only change is that if you still wish to use PulseAudio for some other reason, you will need to install it yourself.

Better handling of other usernames

The default user account in Raspbian has always been called ‘pi’, and a lot of the desktop applications assume that this is the current user. This has been changed for Stretch, so now applications like Raspberry Pi Configuration no longer assume this to be the case. This means, for example, that the option to automatically log in as the ‘pi’ user will now automatically log in with the name of the current user instead.

One other change is how sudo is handled. By default, the ‘pi’ user is set up with passwordless sudo access. We are no longer assuming this to be the case, so now desktop applications which require sudo access will prompt for the password rather than simply failing to work if a user without passwordless sudo uses them.

Scratch 2 SenseHAT extension

In the last Jessie release, we added the offline version of Scratch 2. While Scratch 2 itself hasn’t changed for this release, we have added a new extension to allow the SenseHAT to be used with Scratch 2. Look under ‘More Blocks’ and choose ‘Add an Extension’ to load the extension.

This works with either a physical SenseHAT or with the SenseHAT emulator. If a SenseHAT is connected, the extension will control that in preference to the emulator.

Raspbian Stretch Raspberry Pi

Fix for Broadpwn exploit

A couple of months ago, a vulnerability was discovered in the firmware of the BCM43xx wireless chipset which is used on Pi 3 and Pi Zero W; this potentially allows an attacker to take over the chip and execute code on it. The Stretch release includes a patch that addresses this vulnerability.

There is also the usual set of minor bug fixes and UI improvements – I’ll leave you to spot those!

How to get Raspbian Stretch

As this is a major version upgrade, we recommend using a clean image; these are available from the Downloads page on our site as usual.

Upgrading an existing Jessie image is possible, but is not guaranteed to work in every circumstance. If you wish to try upgrading a Jessie image to Stretch, we strongly recommend taking a backup first – we can accept no responsibility for loss of data from a failed update.

To upgrade, first modify the files /etc/apt/sources.list and /etc/apt/sources.list.d/raspi.list. In both files, change every occurrence of the word ‘jessie’ to ‘stretch’. (Both files will require sudo to edit.)

Then open a terminal window and execute

sudo apt-get update
sudo apt-get -y dist-upgrade

Answer ‘yes’ to any prompts. There may also be a point at which the install pauses while a page of information is shown on the screen – hold the ‘space’ key to scroll through all of this and then hit ‘q’ to continue.

Finally, if you are not using PulseAudio for anything other than Bluetooth audio, remove it from the image by entering

sudo apt-get -y purge pulseaudio*

The post Raspbian Stretch has arrived for Raspberry Pi appeared first on Raspberry Pi.

[$] A canary for timer-expiration functions

Post Syndicated from jake original https://lwn.net/Articles/731082/rss

A bug that allows an attacker to overwrite a function pointer in the kernel
opens up a relatively
easy way to compromise the kernel—doubly so, if an attacker simply
needs to wait for the kernel use the compromised pointer. There are various
techniques that can be used to protect kernel function pointers that are
set at either compile or initialization time, but there are some pointers
that are routinely set as the kernel runs; timer completion functions are a
good example. An RFC patch posted to the kernel-hardening mailing list
would add a way to detect that those function pointers have been changed
in an unexpected way and to stop the kernel from executing that code.

Thank you from Krita

Post Syndicated from ris original https://lwn.net/Articles/731177/rss

Earlier this month we reported that the
Krita Foundation was having some financial difficulties. The Krita
Foundation has an update with thanks to
all who donated. “So, even though we’re going to get another accountant’s bill of about 4500 euros, we’ve still got quite a surplus! As of this moment, we have €29,657.44 in our savings account!

That means that we don’t need to do a fund raiser in September. Like we said, we’ve still got some features to finish.”

What’s the Diff: Programs, Processes, and Threads

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/whats-the-diff-programs-processes-and-threads/

let's talk about Threads

How often have you heard the term threading in relation to a computer program, but you weren’t exactly sure what it meant? How about processes? You likely understand that a thread is somehow closely related to a program and a process, but if you’re not a computer science major, maybe that’s as far as your understanding goes.

Knowing what these terms mean is absolutely essential if you are a programmer, but an understanding of them also can be useful to the average computer user. Being able to look at and understand the Activity Monitor on the Macintosh, the Task Manager on Windows, or Top on Linux can help you troubleshoot which programs are causing problems on your computer, or whether you might need to install more memory to make your system run better.

Let’s take a few minutes to delve into the world of computer programs and sort out what these terms mean. We’ll simplify and generalize some of the ideas, but the general concepts we cover should help clarify the difference between the terms.

Programs

First of all, you probably are aware that a program is the code that is stored on your computer that is intended to fulfill a certain task. There are many types of programs, including programs that help your computer function and are part of the operating system, and other programs that fulfill a particular job. These task-specific programs are also known as “applications,” and can include programs such as word processing, web browsing, or emailing a message to another computer.

Program

Programs are typically stored on disk or in non-volatile memory in a form that can be executed by your computer. Prior to that, they are created using a programming language such as C, Lisp, Pascal, or many others using instructions that involve logic, data and device manipulation, recurrence, and user interaction. The end result is a text file of code that is compiled into binary form (1’s and 0’s) in order to run on the computer. Another type of program is called “interpreted,” and instead of being compiled in advance in order to run, is interpreted into executable code at the time it is run. Some common, typically interpreted programming languages, are Python, PHP, JavaScript, and Ruby.

The end result is the same, however, in that when a program is run, it is loaded into memory in binary form. The computer’s CPU (Central Processing Unit) understands only binary instructions, so that’s the form the program needs to be in when it runs.

Perhaps you’ve heard the programmer’s joke, “There are only 10 types of people in the world, those who understand binary, and those who don’t.”

Binary is the native language of computers because an electrical circuit at its basic level has two states, on or off, represented by a one or a zero. In the common numbering system we use every day, base 10, each digit position can be anything from 0 to 9. In base 2 (or binary), each position is either a 0 or a 1. (In a future blog post we might cover quantum computing, which goes beyond the concept of just 1’s and 0’s in computing.)

Decimal—Base 10 Binary—Base 2
0 0000
1 0001
2 0010
3 0011
4 0100
5 0101
6 0110
7 0111
8 1000
9 1001

How Processes Work

The program has been loaded into the computer’s memory in binary form. Now what?

An executing program needs more than just the binary code that tells the computer what to do. The program needs memory and various operating system resources that it needs in order to run. A “process” is what we call a program that has been loaded into memory along with all the resources it needs to operate. The “operating system” is the brains behind allocating all these resources, and comes in different flavors such as macOS, iOS, Microsoft Windows, Linux, and Android. The OS handles the task of managing the resources needed to turn your program into a running process.

Some essential resources every process needs are registers, a program counter, and a stack. The “registers” are data holding places that are part of the computer processor (CPU). A register may hold an instruction, a storage address, or other kind of data needed by the process. The “program counter,” also called the “instruction pointer,” keeps track of where a computer is in its program sequence. The “stack” is a data structure that stores information about the active subroutines of a computer program and is used as scratch space for the process. It is distinguished from dynamically allocated memory for the process that is known as “the heap.”

diagram of how processes work

There can be multiple instances of a single program, and each instance of that running program is a process. Each process has a separate memory address space, which means that a process runs independently and is isolated from other processes. It cannot directly access shared data in other processes. Switching from one process to another requires some time (relatively) for saving and loading registers, memory maps, and other resources.

This independence of processes is valuable because the operating system tries its best to isolate processes so that a problem with one process doesn’t corrupt or cause havoc with another process. You’ve undoubtedly run into the situation in which one application on your computer freezes or has a problem and you’ve been able to quit that program without affecting others.

How Threads Work

So, are you still with us? We finally made it to threads!

A thread is the unit of execution within a process. A process can have anywhere from just one thread to many threads.

Process vs. Thread

diagram of threads in a process over time

When a process starts, it is assigned memory and resources. Each thread in the process shares that memory and resources. In single-threaded processes, the process contains one thread. The process and the thread are one and the same, and there is only one thing happening.

In multithreaded processes, the process contains more than one thread, and the process is accomplishing a number of things at the same time (technically, it’s almost at the same time—read more on that in the “What about Parallelism and Concurrency?” section below).

diagram of single and multi-treaded process

We talked about the two types of memory available to a process or a thread, the stack and the heap. It is important to distinguish between these two types of process memory because each thread will have its own stack, but all the threads in a process will share the heap.

Threads are sometimes called lightweight processes because they have their own stack but can access shared data. Because threads share the same address space as the process and other threads within the process, the operational cost of communication between the threads is low, which is an advantage. The disadvantage is that a problem with one thread in a process will certainly affect other threads and the viability of the process itself.

Threads vs. Processes

So to review:

  1. The program starts out as a text file of programming code,
  2. The program is compiled or interpreted into binary form,
  3. The program is loaded into memory,
  4. The program becomes one or more running processes.
  5. Processes are typically independent of each other,
  6. While threads exist as the subset of a process.
  7. Threads can communicate with each other more easily than processes can,
  8. But threads are more vulnerable to problems caused by other threads in the same process.

Processes vs. Threads — Advantages and Disadvantages

Process Thread
Processes are heavyweight operations Threads are lighter weight operations
Each process has its own memory space Threads use the memory of the process they belong to
Inter-process communication is slow as processes have different memory addresses Inter-thread communication can be faster than inter-process communication because threads of the same process share memory with the process they belong to
Context switching between processes is more expensive Context switching between threads of the same process is less expensive
Processes don’t share memory with other processes Threads share memory with other threads of the same process

What about Concurrency and Parallelism?

A question you might ask is whether processes or threads can run at the same time. The answer is: it depends. On a system with multiple processors or CPU cores (as is common with modern processors), multiple processes or threads can be executed in parallel. On a single processor, though, it is not possible to have processes or threads truly executing at the same time. In this case, the CPU is shared among running processes or threads using a process scheduling algorithm that divides the CPU’s time and yields the illusion of parallel execution. The time given to each task is called a “time slice.” The switching back and forth between tasks happens so fast it is usually not perceptible. The terms parallelism (true operation at the same time) and concurrency (simulated operation at the same time), distinguish between the two type of real or approximate simultaneous operation.

diagram of concurrency and parallelism

Why Choose Process over Thread, or Thread over Process?

So, how would a programmer choose between a process and a thread when creating a program in which she wants to execute multiple tasks at the same time? We’ve covered some of the differences above, but let’s look at a real world example with a program that many of us use, Google Chrome.

When Google was designing the Chrome browser, they needed to decide how to handle the many different tasks that needed computer, communications, and network resources at the same time. Each browser window or tab communicates with multiple servers on the internet to retrieve text, programs, graphics, audio, video, and other resources, and renders that data for display and interaction with the user. In addition, the browser can open many windows, each with many tasks.

Google had to decide how to handle that separation of tasks. They chose to run each browser window in Chrome as a separate process rather than a thread or many threads, as is common with other browsers. Doing that brought Google a number of benefits. Running each window as a process protects the overall application from bugs and glitches in the rendering engine and restricts access from each rendering engine process to others and to the rest of the system. Isolating JavaScript programs in a process prevents them from running away with too much CPU time and memory, and making the entire browser non-responsive.

Google made the calculated trade-off with a multi-processing design as starting a new process for each browser window has a higher fixed cost in memory and resources than using threads. They were betting that their approach would end up with less memory bloat overall.

Using processes instead of threads provides better memory usage when memory gets low. An inactive window is treated as a lower priority by the operating system and becomes eligible to be swapped to disk when memory is needed for other processes, helping to keep the user-visible windows more responsive. If the windows were threaded, it would be more difficult to separate the used and unused memory as cleanly, wasting both memory and performance.

You can read more about Google’s design decisions on Google’s Chromium Blog or on the Chrome Introduction Comic.

The screen capture below shows the Google Chrome processes running on a MacBook Air with many tabs open. Some Chrome processes are using a fair amount of CPU time and resources, and some are using very little. You can see that each process also has many threads running as well.

activity monitor of Google Chrome

The Activity Monitor or Task Manager on your system can be a valuable ally in helping fine-tune your computer or troubleshooting problems. If your computer is running slowly, or a program or browser window isn’t responding for a while, you can check its status using the system monitor. Sometimes you’ll see a process marked as “Not Responding.” Try quitting that process and see if your system runs better. If an application is a memory hog, you might consider choosing a different application that will accomplish the same task.

Windows Task Manager view

Made it This Far?

We hope this Tron-like dive into the fascinating world of computer programs, processes, and threads has helped clear up some questions you might have had.

The next time your computer is running slowly or an application is acting up, you know your assignment. Fire up the system monitor and take a look under the hood to see what’s going on. You’re in charge now.

We love to hear from you

Are you still confused? Have questions? If so, please let us know in the comments. And feel free to suggest topics for future blog posts.

The post What’s the Diff: Programs, Processes, and Threads appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Community Profile: David Pride

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/community-profile-david-pride/

This column is from The MagPi issue 55. You can download a PDF of the full issue for free, or subscribe to receive the print edition in your mailbox or the digital edition on your tablet. All proceeds from the print and digital editions help the Raspberry Pi Foundation achieve its charitable goals.

David Pride’s experiences in computer education came slightly later in life. He admits to not being a grade-A student: he left school with few qualifications, unable to pursue further education at university. There was, however, a teacher who instilled in him a passion for computers and coding which would stick with him indefinitely.

David Pride The MagPi Raspberry Pi Community Profile

David joined us at the St James’s Palace community celebration, mingling with the likes of the Duke of York, plus organisers of Jams and clubs, such as Grace and Femi

Welcome to the Community

Twenty years later, back in 2012, David heard of the Raspberry Pi – a soon-to-be-released “new little marvel” that he instantly fell for, head first. Despite a lack of knowledge in Linux and Python, he experimented and had fun. He found a Raspberry Jam and, with it, Pi enthusiasts like Mike Horne and Peter Onion. The projects on display at the Jam were enough to push David further into the Raspberry Pi rabbit hole and, after working his way through several Python books, he began to take steps into the world of formal higher education.

David Pride The MagPi Raspberry Pi Community Profile

David’s determination to access and complete further education in computing has earned him a three-year PhD studentship. Not bad for a “lousy student”

Back to School

With a Mooc qualification from Rice University under his belt, he continued to improve upon his self-taught knowledge, and was fortunate enough to be accepted to study for a master’s degree in Computer Science at the University of Hertfordshire. With a distinction for his final dissertation, David completed the course with an overall distinction for his MSc, and was recently awarded a fully funded PhD studentship with The Open University’s Knowledge Media Institute.

David Pride The MagPi Raspberry Pi Community Profile

Self-playing xylophones, Wiimote air drums, Lego sorters, Pi Wars robots, and more. David is continually hacking toys, giving them new Pi-powered life

Maker of things

The portfolio of projects that helped him to achieve his many educational successes has provided regular retweet material for the Raspberry Pi Twitter account, and we’ve highlighted his fun, imaginative work on this blog before. His builds have travelled to a range of Jams and made their way to the Raspberry Pi and Code Club stands at the Bett Show, as well as to our birthday celebrations.

David Pride The MagPi Raspberry Pi Community Profile

“Pi & Chips – with a little extra source”

His website, the pun-tastic Pi and Chips, is home to the majority of his work; David also links to YouTube videos and walk-throughs of his projects, and relates his experiences at various events. If you’ve followed any of the action across the Raspberry Pi social media channels – or indeed read any previous issues of The MagPi magazine – you’ll no doubt have seen a couple of David’s projects.

David Pride The MagPi Raspberry Pi Community Profile 4-Bot

Many readers will have come across the wonderful 4-Bot before, and it has even made an appearance alongside David in a recent Bloomberg interview. Considering the trillions of possible game positions, David made a compromise and, if you’re lucky, you may just be able to beat it

The 4-Bot, a robotic second player for the family game Connect Four, allows people to go head to head with a Pi-powered robotic arm. Using a Python imaging library, the 4-Bot splits the game grid into 42 squares, and recognises them as being red, yellow, or empty by reading the RGB value of the space. Using the minimax algorithm, 4-Bot is able to play each move within 25 seconds. Believe us when we say that it’s not as easy to beat as you’d hope. Then there’s his more recent air drum kit, which uses an old toy found at a car boot sale together with a Wiimote to make a functional air drum that showcases David’s toy-hacking abilities… and his complete lack of rhythm. He does fare much better on his homemade laser harp, though!

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OK Google, be aesthetically pleasing

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/aesthetically-pleasing-ok-google/

Maker Andrew Jones took a Raspberry Pi and the Google Assistant SDK and created a gorgeous-looking, and highly functional, alternative to store-bought smart speakers.

Raspberry Pi Google AI Assistant

In this video I get an “Ok Google” voice activated AI assistant running on a raspberry pi. I also hand make a nice wooden box for it to live in.

OK Google, what are you?

Google Assistant is software of the same ilk as Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana. It’s a virtual assistant that allows you to request information, play audio, and control smart home devices via voice commands.

Infinite Looping Siri, Alexa and Google Home

One can barely see the iPhone’s screen. That’s because I have a privacy protection screen. Sorry, did not check the camera angle. Learn how to create your own loop, why we put Cortana out of the loop, and how to train Siri to an artificial voice: https://www.danrl.com/2016/12/01/looping-ais-siri-alexa-google-home.html

You probably have a digital assistant on your mobile phone, and if you go to the home of someone even mildly tech-savvy, you may see a device awaiting commands via a wake word such the device’s name or, for the Google Assistant, the phrase “OK, Google”.

Homebrew versions

Understanding the maker need to ‘put tech into stuff’ and upgrade everyday objects into everyday objects 2.0, the creators of these virtual assistants have allowed access for developers to run their software on devices such as the Raspberry Pi. This means that your common-or-garden homemade robot can now be controlled via voice, and your shed-built home automation system can have easy-to-use internet connectivity via a reliable, multi-device platform.

Andrew’s Google Assistant build

Andrew gives a peerless explanation of how the Google Assistant works:

There’s Google’s Cloud. You log into Google’s Cloud and you do a bunch of cloud configuration cloud stuff. And then on the Raspberry Pi you install some Python software and you do a bunch of configuration. And then the cloud and the Pi talk the clouds kitten rainbow protocol and then you get a Google AI assistant.

It all makes perfect sense. Though for more extra detail, you could always head directly to Google.

Andrew Jones Raspberry Pi OK Google Assistant

I couldn’t have explained it better myself

Andrew decided to take his Google Assistant-enabled Raspberry Pi and create a new body for it. One that was more aesthetically pleasing than the standard Pi-inna-box. After wiring his build and cannibalising some speakers and a microphone, he created a sleek, wooden body that would sit quite comfortably in any Bang & Olufsen shop window.

Find the entire build tutorial on Instructables.

Make your own

It’s more straightforward than Andrew’s explanation suggests, we promise! And with an array of useful resources online, you should be able to incorporate your choice of virtual assistants into your build.

There’s The Raspberry Pi Guy’s tutorial on setting up Amazon Alexa on the Raspberry Pi. If you’re looking to use Siri on your Pi, YouTube has a plethora of tutorials waiting for you. And lastly, check out Microsoft’s site for using Cortana on the Pi!

If you’re looking for more information on Google Assistant, check out issue 57 of The MagPi Magazine, free to download as a PDF. The print edition of this issue came with a free AIY Projects Voice Kit, and you can sign up for The MagPi newsletter to be the first to know about the kit’s availability for purchase.

The post OK Google, be aesthetically pleasing appeared first on Raspberry Pi.

SAML Raider – SAML2 Security Testing Burp Extension

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

SAML Raider is a Burp Suite extension for SAML2 security testing, it contains two core functionalities – Manipulating SAML Messages and managing X.509 certificates. The extension is divided into two parts, a SAML message editor and a certificate management tool. Features Message Editor Features of the SAML Raider message editor: Sign SAML Messages…

Read the full post at darknet.org.uk

AWS CloudHSM Update – Cost Effective Hardware Key Management at Cloud Scale for Sensitive & Regulated Workloads

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-cloudhsm-update-cost-effective-hardware-key-management/

Our customers run an incredible variety of mission-critical workloads on AWS, many of which process and store sensitive data. As detailed in our Overview of Security Processes document, AWS customers have access to an ever-growing set of options for encrypting and protecting this data. For example, Amazon Relational Database Service (RDS) supports encryption of data at rest and in transit, with options tailored for each supported database engine (MySQL, SQL Server, Oracle, MariaDB, PostgreSQL, and Aurora).

Many customers use AWS Key Management Service (KMS) to centralize their key management, with others taking advantage of the hardware-based key management, encryption, and decryption provided by AWS CloudHSM to meet stringent security and compliance requirements for their most sensitive data and regulated workloads (you can read my post, AWS CloudHSM – Secure Key Storage and Cryptographic Operations, to learn more about Hardware Security Modules, also known as HSMs).

Major CloudHSM Update
Today, building on what we have learned from our first-generation product, we are making a major update to CloudHSM, with a set of improvements designed to make the benefits of hardware-based key management available to a much wider audience while reducing the need for specialized operating expertise. Here’s a summary of the improvements:

Pay As You Go – CloudHSM is now offered under a pay-as-you-go model that is simpler and more cost-effective, with no up-front fees.

Fully Managed – CloudHSM is now a scalable managed service; provisioning, patching, high availability, and backups are all built-in and taken care of for you. Scheduled backups extract an encrypted image of your HSM from the hardware (using keys that only the HSM hardware itself knows) that can be restored only to identical HSM hardware owned by AWS. For durability, those backups are stored in Amazon Simple Storage Service (S3), and for an additional layer of security, encrypted again with server-side S3 encryption using an AWS KMS master key.

Open & Compatible  – CloudHSM is open and standards-compliant, with support for multiple APIs, programming languages, and cryptography extensions such as PKCS #11, Java Cryptography Extension (JCE), and Microsoft CryptoNG (CNG). The open nature of CloudHSM gives you more control and simplifies the process of moving keys (in encrypted form) from one CloudHSM to another, and also allows migration to and from other commercially available HSMs.

More Secure – CloudHSM Classic (the original model) supports the generation and use of keys that comply with FIPS 140-2 Level 2. We’re stepping that up a notch today with support for FIPS 140-2 Level 3, with security mechanisms that are designed to detect and respond to physical attempts to access or modify the HSM. Your keys are protected with exclusive, single-tenant access to tamper-resistant HSMs that appear within your Virtual Private Clouds (VPCs). CloudHSM supports quorum authentication for critical administrative and key management functions. This feature allows you to define a list of N possible identities that can access the functions, and then require at least M of them to authorize the action. It also supports multi-factor authentication using tokens that you provide.

AWS-Native – The updated CloudHSM is an integral part of AWS and plays well with other tools and services. You can create and manage a cluster of HSMs using the AWS Management Console, AWS Command Line Interface (CLI), or API calls.

Diving In
You can create CloudHSM clusters that contain 1 to 32 HSMs, each in a separate Availability Zone in a particular AWS Region. Spreading HSMs across AZs gives you high availability (including built-in load balancing); adding more HSMs gives you additional throughput. The HSMs within a cluster are kept in sync: performing a task or operation on one HSM in a cluster automatically updates the others. Each HSM in a cluster has its own Elastic Network Interface (ENI).

All interaction with an HSM takes place via the AWS CloudHSM client. It runs on an EC2 instance and uses certificate-based mutual authentication to create secure (TLS) connections to the HSMs.

At the hardware level, each HSM includes hardware-enforced isolation of crypto operations and key storage. Each customer HSM runs on dedicated processor cores.

Setting Up a Cluster
Let’s set up a cluster using the CloudHSM Console:

I click on Create cluster to get started, select my desired VPC and the subnets within it (I can also create a new VPC and/or subnets if needed):

Then I review my settings and click on Create:

After a few minutes, my cluster exists, but is uninitialized:

Initialization simply means retrieving a certificate signing request (the Cluster CSR):

And then creating a private key and using it to sign the request (these commands were copied from the Initialize Cluster docs and I have omitted the output. Note that ID identifies the cluster):

$ openssl genrsa -out CustomerRoot.key 2048
$ openssl req -new -x509 -days 365 -key CustomerRoot.key -out CustomerRoot.crt
$ openssl x509 -req -days 365 -in ID_ClusterCsr.csr   \
                              -CA CustomerRoot.crt    \
                              -CAkey CustomerRoot.key \
                              -CAcreateserial         \
                              -out ID_CustomerHsmCertificate.crt

The next step is to apply the signed certificate to the cluster using the console or the CLI. After this has been done, the cluster can be activated by changing the password for the HSM’s administrative user, otherwise known as the Crypto Officer (CO).

Once the cluster has been created, initialized and activated, it can be used to protect data. Applications can use the APIs in AWS CloudHSM SDKs to manage keys, encrypt & decrypt objects, and more. The SDKs provide access to the CloudHSM client (running on the same instance as the application). The client, in turn, connects to the cluster across an encrypted connection.

Available Today
The new HSM is available today in the US East (Northern Virginia), US West (Oregon), US East (Ohio), and EU (Ireland) Regions, with more in the works. Pricing starts at $1.45 per HSM per hour.

Jeff;

AWS Config Update – New Managed Rules to Secure S3 Buckets

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-config-update-new-managed-rules-to-secure-s3-buckets/

AWS Config captures the state of your AWS resources and the relationships between them. Among other features, it allows you to select a resource and then view a timeline of configuration changes that affect the resource (read Track AWS Resource Relationships With AWS Config to learn more).

AWS Config rules extends Config with a powerful rule system, with support for a “managed” collection of AWS rules as well as custom rules that you write yourself (my blog post, AWS Config Rules – Dynamic Compliance Checking for Cloud Resources, contains more info). The rules (AWS Lambda functions) represent the ideal (properly configured and compliant) state of your AWS resources. The appropriate functions are invoked when a configuration change is detected and check to ensure compliance.

You already have access to about three dozen managed rules. For example, here are some of the rules that check your EC2 instances and related resources:

Two New Rules
Today we are adding two new managed rules that will help you to secure your S3 buckets. You can enable these rules with a single click. The new rules are:

s3-bucket-public-write-prohibited – Automatically identifies buckets that allow global write access. There’s rarely a reason to create this configuration intentionally since it allows
unauthorized users to add malicious content to buckets and to delete (by overwriting) existing content. The rule checks all of the buckets in the account.

s3-bucket-public-read-prohibited – Automatically identifies buckets that allow global read access. This will flag content that is publicly available, including web sites and documentation. This rule also checks all buckets in the account.

Like the existing rules, the new rules can be run on a schedule or in response to changes detected by Config. You can see the compliance status of all of your rules at a glance:

Each evaluation runs in a matter of milliseconds; scanning an account with 100 buckets will take less than a minute. Behind the scenes, the rules are evaluated by a reasoning engine that uses some leading-edge constraint solving techniques that can, in many cases, address NP-complete problems in polynomial time (we did not resolve P versus NP; that would be far bigger news). This work is part of a larger effort within AWS, some of which is described in a AWS re:Invent presentation: Automated Formal Reasoning About AWS Systems:

Now Available
The new rules are available now and you can start using them today. Like the other rules, they are priced at $2 per rule per month.

Jeff;

A Poloniex / Bitfinex cryptocurrency lending bot

Post Syndicated from Григор original http://www.gatchev.info/blog/?p=2074

… offering its services. Its site is http://beebot.zavinagi.org .

The bot already has some clients and manages their loans quite well. (As well as mine.) If you want your crypto to bring you the best interest that can be obtained, with no effort from you at all, be welcome! 🙂

The bot can manage your cryptocurrencies at the popular exchanges Poloniex and Bitfinex. All it needs from you is an API key that allows it to manage loans (and does NOT allow withdrawing or trading the funds!). Has plenty of settings that allow tuning its work to your taste. Has also a lot of loaning-related data, both current and historical, that you can find nowhere else.

Is it good? I believe so. In my comparisons, it appears at least as good as the best and most established lending bots around. Constant tracking of the optimal loan interest is only where it starts. It varies the lending period to ensure biggest probability for and most exposure to high-interest lending. It analyses the situation and tries to predict optimal interest movement. It tries to detect attempts to manipulate the lending interests and takes appropriate measures… The list is pretty long.

The usage tax is 10% of the interest earned by the loans secured by the bot. This is only a small part of the benefits it provides. If you would like to manage through it bigger sums (eg. BTC 100 and up), we can negotiate a lower tax – write me at ‘grigor’ in the site you read this blog post in. 🙂