Tag Archives: mit

[$] The supposed decline of copyleft

Post Syndicated from jake original https://lwn.net/Articles/731722/rss

At DebConf17, John Sullivan, the executive director of the FSF,
gave a talk on the supposed decline of the use of
copyleft licenses in free-software projects. In his presentation, Sullivan
questioned the notion that permissive licenses, like the BSD or MIT
licenses, are gaining ground at the expense of the traditionally dominant
copyleft licenses from the FSF. While there does seem to be a rise in
the use of permissive licenses, in general, there are several possible
explanations for
the phenomenon.

Bitcoin Anonymity Compromised By Most Vendors

Post Syndicated from Darknet original http://feedproxy.google.com/~r/darknethackers/~3/ONgF504Ytqs/

Cryptocurrency is getting a lot of press lately and some researchers dug a little bit deeper in Bitcoin anonymity as it’s a touted selling point for most cryptocurrencies. It’s not a problem with Bitcoin itself, or any other coin, more the fact that shopping cart implementations and analytics systems aren’t built with the anonymity of…

Read the full post at darknet.org.uk

ROI is not a cybersecurity concept

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/08/roi-is-not-cybersecurity-concept.html

In the cybersecurity community, much time is spent trying to speak the language of business, in order to communicate to business leaders our problems. One way we do this is trying to adapt the concept of “return on investment” or “ROI” to explain why they need to spend more money. Stop doing this. It’s nonsense. ROI is a concept pushed by vendors in order to justify why you should pay money for their snake oil security products. Don’t play the vendor’s game.

The correct concept is simply “risk analysis”. Here’s how it works.

List out all the risks. For each risk, calculate:

  • How often it occurs.
  • How much damage it does.
  • How to mitigate it.
  • How effective the mitigation is (reduces chance and/or cost).
  • How much the mitigation costs.

If you have risk of something that’ll happen once-per-day on average, costing $1000 each time, then a mitigation costing $500/day that reduces likelihood to once-per-week is a clear win for investment.

Now, ROI should in theory fit directly into this model. If you are paying $500/day to reduce that risk, I could use ROI to show you hypothetical products that will …

  • …reduce the remaining risk to once-per-month for an additional $10/day.
  • …replace that $500/day mitigation with a $400/day mitigation.

But this is never done. Companies don’t have a sophisticated enough risk matrix in order to plug in some ROI numbers to reduce cost/risk. Instead, ROI is a calculation is done standalone by a vendor pimping product, or a security engineer building empires within the company.

If you haven’t done risk analysis to begin with (and almost none of you have), then ROI calculations are pointless.

But there are further problems. This is risk analysis as done in industries like oil and gas, which have inanimate risk. Almost all their risks are due to accidental failures, like in the Deep Water Horizon incident. In our industry, cybersecurity, risks are animate — by hackers. Our risk models are based on trying to guess what hackers might do.

An example of this problem is when our drug company jacks up the price of an HIV drug, Anonymous hackers will break in and dump all our financial data, and our CFO will go to jail. A lot of our risks come now from the technical side, but the whims and fads of the hacker community.

Another example is when some Google researcher finds a vuln in WordPress, and our website gets hacked by that three months from now. We have to forecast not only what hackers can do now, but what they might be able to do in the future.

Finally, there is this problem with cybersecurity that we really can’t distinguish between pesky and existential threats. Take ransomware. A lot of large organizations have just gotten accustomed to just wiping a few worker’s machines every day and restoring from backups. It’s a small, pesky problem of little consequence. Then one day a ransomware gets domain admin privileges and takes down the entire business for several weeks, as happened after #nPetya. Inevitably our risk models always come down on the high side of estimates, with us claiming that all threats are existential, when in fact, most companies continue to survive major breaches.

These difficulties with risk analysis leads us to punting on the problem altogether, but that’s not the right answer. No matter how faulty our risk analysis is, we still have to go through the exercise.

One model of how to do this calculation is architecture. We know we need a certain number of toilets per building, even without doing ROI on the value of such toilets. The same is true for a lot of security engineering. We know we need firewalls, encryption, and OWASP hardening, even without specifically doing a calculation. Passwords and session cookies need to go across SSL. That’s the starting point from which we start to analysis risks and mitigations — what we need beyond SSL, for example.

So stop using “ROI”, or worse, the abomination “ROSI”. Start doing risk analysis.

Many Film Students Pirate Films for Their Courses

Post Syndicated from Ernesto original https://torrentfreak.com/many-film-students-pirate-films-for-their-courses-170822/

Hollywood leaves no opportunity unused in stressing that piracy is hurting the livelihoods of millions of people who work in the movie industry.

Despite these efforts, many people who have or aspire to a career in the movie industry regularly turn to pirate sites. This includes film students who are required to watch movies for class assignments.

New research by Wendy Rodgers, Humanities Research Liaison Librarian at Memorial University of Newfoundland, reveals that piracy is a common occurrence among film students in Canada. This is the conclusion of an extensive survey among students, professors, and librarians at several large universities.

The results, outlined in a paper titled “Buy, Borrow, or Steal? Film Access for Film Studies Students,” show that students know that piracy is illegal. However, more than half admit to having downloaded movies in the past because it’s more convenient, cheaper, or the only option.

“92% of students know that downloading copyrighted films through P2P or other free online methods is illegal. Yet 60% have done it anyway, reportedly turning to illegal sources because legal channels were inconvenient, expensive, or unavailable,” Rodgers writes.

The students are not alone in their deviant behavior. The study reveals that 17% of librarians and 14% of faculty have also pirated films.

Moving on, the students were asked about their methods to access films that are required course material. P2P downloading is popular here as well, with 42% admitting that they “always” or “usually” pirate these films. Using “free websites” was also common for 51% of the students, but this could include both legal platforms and pirate sites.

Buying or renting a DVD is significantly less popular, with 8% and 2% respectively. The same is true for lending from the university library reserve desk, which scored only 22%.

For staff and librarians, it doesn’t come as a surprise that many students download content illegally. They think the majority of the students use pirate sources, and one of the surveyed professors admits to having an unofficial “don’t ask, don’t tell” policy

“I have made it my policy not to ask HOW the students are viewing the films, since I know most are doing so illegally. I do not encourage this, and I ensure legal access is available, but many students are so used to illegally downloading media that their first instinct is to view the films that way.”

Among librarians, the piracy habits of students are also well known. The paper quotes a librarian who sometimes points out that certain films are only available on pirate sites, without actively encouraging students to break the law.

“If a film is out of print or otherwise not legally available in Canada, and if the film might otherwise be available online by nefarious networking means, I will inform patrons of the fact, and advise them that I would never in good conscience advise them to avail themselves of those means.

“You catch my drift? If they’re looking for the film it is because they need it for academic purposes, and our protectionist IP regime is sometimes an unfortunate hindrance,” the librarian stated.

The paper’s main conclusion is that piracy is widespread among film students, in part because of lacking legal options. It recommends that libraries increase the legal availability of required course material, and lobby the movie industry and government for change.

“Librarians and educators need to do more to support students, recognizing that the system – not the student – is dysfunctional,” Rodgers notes.

While students certainly have their own responsibilities, it would make sense to increase streaming options, digitize DVDs when legally possible, and screen more films in class, for example.

“Buy, Borrow, or Steal? Film Access for Film Studies Students” was accepted for publication and will appear in a future issue of the College & Research Libraries journal.

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

An Invitation for CrashPlan Customers: Try Backblaze

Post Syndicated from Gleb Budman original https://www.backblaze.com/blog/crashplan-alternative-backup-solution/

Welcome CrashPlan Users
With news coming out this morning of CrashPlan exiting the consumer market, we know some of you may be considering which backup provider to call home. We welcome you to try us.

For over a decade, Backblaze has provided unlimited cloud backup for Windows and Macintosh computers at $5 per month (or $50 per year).

Backblaze is excellent if you’re looking for the cheapest online backup option that still offers serious file protection.” — Dann Berg, Tom’s Guide.

That’s it. Ready to make sure your data is safe? Try Backblaze for free — it’ll take you less than a minute and you don’t need a credit card to start protecting your data.

Our customers don’t have to choose between competing feature sets or hard to understand fine print. There are no extra charges and no limits on the size of your files — no matter how many videos you want to back up. And when we say unlimited, we mean unlimited; there are no restrictions on files, gigabytes, or restores. Customers also love the choices they have for getting their data back — web, mobile apps, and our free Restore by Mail option. We’re also the fastest to back up your data. While other services throttle your upload speeds, we want to get you protected as quickly as possible.

Backblaze vs. Carbonite

We know that CrashPlan is encouraging customers to look at Carbonite as an alternative. We would like to offer you another option: Backblaze. We cost less, we offer more, we store over 350 Petabytes of data, we have restored over 20 billion files, and customers in over 120 countries around the world trust us with their data.

Backblaze Carbonite Basic Carbonite Prime
Price per Computer $50/year $59.99/year $149.99/year
Back Up All User Data By Default – No Picking And Choosing Yes No No
Automatically Back Up Files Of Any Size, Including Videos Yes No Yes1
Back Up Multiple USB External Hard Drives Yes No No
Restore by Mail for Free Yes No No
Locate Computer Yes No No
Manage Families & Teams Yes No No
Protect Accounts Via Two Factor VerificationSMS & Authenticator Apps Yes No No
Protect Data Via Private Encryption Key Yes No No2
(1) All videos and files over 4GB require manual selection.  (2) Available on Windows Only

To get just some of the features offered by Backblaze for $50/year, you would need to purchase Carbonite Prime at $149.99/year.

Reminder: Sync is Not Backup

“Backblaze is my favorite online backup service, mostly because everything about it is so simple, especially its pricing and software.“ Tim Fisher — Lifewire: 22 Online Backup Services Reviewed

Of course, there are plenty of options in the marketplace. We encourage you to choose one to make sure you stay backed up. One thing we tell our own friends and family: sync is not backup.

If you’re considering using a sync service — Dropbox, Google Drive, OneDrive, iCloud, etc. — you should know that these services are not designed to back up all your data. Typically, they only sync data from a specific directory or folder. If the service detects a file was deleted from your sync folder, it also will delete it from their server, and you’re out of luck. In addition, most don’t support external drives and have tiered pricing that gets quite expensive.

Backblaze is the Simple, Reliable, and Affordable Choice for Unlimited Backup of All Your Data
People have trusted Backblaze to protect their digital photos, music, movies, and documents for the past 10 years. We look forward to doing the same for your valuable data.

Your CrashPlan service may not be getting shut off today. But there’s no reason to wait until your data is at risk. Try Backblaze for FREE today — all you need to do is pick an email/password and click download.

The post An Invitation for CrashPlan Customers: Try Backblaze appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Mod your Nerf gun with a Pi

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/mod-nerf-gun-pi/

Michael Darby, who blogs at 314reactor, has created a new Raspberry Pi build, and it’s pretty darn cool. Though it’s not the first Raspberry Pi-modded Nerf gun we’ve seen, it’s definitely one of the most complex!

Nerf Gun Ammo Counter / Range Finder – Raspberry Pi

An ammo counter and range finder made from a Raspberry Pi for a Nerf Gun.

Nerf guns

Nerf guns are toy dart guns that have been on the market since the early 1990s. They are popular with kids and adults who enjoy playing paintball, laser tag, and first-person shooter video games. Michael loves Nerf guns, and he wanted to give his toy a sci-fi overhaul, making it look and function more like a gun that an avatar might use in Half-Life, Quake, or Doom.

Modding a Nerf gun

A busy and creative member of the Raspberry Pi community, Michael has previously delighted us with his Windows 98 wristwatch. Now, he has upgraded his Nerf gun with a rangefinder and an ammo counter by adding a Pi, a Pimoroni Rainbow HAT, and some sensors.

Setting up a rangefinder was straightforward. Michael fixed an ultrasonic distance sensor pointing in the direction of the gun’s barrel. Live information about how far away he is from his target is shown on the Rainbow HAT’s alphanumeric display.

View of Michael Darby's nerf gun range finder

To create an ammo counter, Michael had to follow a more circuitous route. Since he couldn’t think of a way to read out how many darts are in the Nerf gun’s magazine, he ended up counting how many darts have been shot instead. This data is collected via a proximity sensor, a device that can measure shorter distances than an ultrasonic sensor. Michael aimed the sensor towards the end of the barrel, attaching it with Blu-Tack.

View of Michael Darby's nerf gun proximity sensor

The number of shots left in the magazine is indicated by the seven LEDs above the Rainbow HAT’s alphanumeric display. The countdown works for more than seven darts, thanks to colour coding: the LEDs count down first in red, then in orange, and finally in green.

In a Python script running on the Pi, Michael has included a default number of shots per magazine. When he changes a magazine, he uses one of the HAT’s buttons as a ‘Reload’ button, resetting the counter. He has also set up the HAT so that the number of available shots can be entered manually instead.

Nerf gun modding tutorial

On Michael’s blog you will find a thorough step-by-step guide to how he created this build. He has also included his code, and links to all the components, software installation guides, and test scripts he has used. So head on over there if you’re keen to mod your own nerf gun like this, and take a look at some of his other projects while you’re there!

Michael welcomes suggestions for how to improve upon his mods, especially for how to count shots in a magazine automatically. Do you have an idea? Let usand himknow in the comments!

Toy mods

Over the years, we’ve covered quite a few fun toy upgrades, and some that may have to be approached with caution. The Pi-powered busy board for babies, the ‘weaponized’ teddy bear, and the inevitable smart Fisher Price phone are just a few from our archives.

What’s your favourite childhood toy, and how could it be improved by the addition of a Pi? Share your ideas with us in the comments below.

The post Mod your Nerf gun with a Pi appeared first on Raspberry Pi.

Court Cracks Down on ‘Future’ Pirate Mayweather-McGregor Streams

Post Syndicated from Ernesto original https://torrentfreak.com/court-cracks-down-on-future-pirate-mayweather-mcgregor-streams-170821/

This weekend, the undefeated Floyd Mayweather Jr. will go head-to-head with UFC lightweight champion Conor McGregor at the T-Mobile Arena in Las Vegas.

The fight is not just about prestige, but also about money. Some predict that the unusual matchup could pull in a staggering one billion dollars.

A significant portion of this will go to each of the fighters, but rightsholders such as Showtime benefit as well.

People who want to stream the event live over the Internet will have to cough up between $89.95 and $99.99. This will generate millions of dollars in revenue but the numbers would be even higher if it wasn’t so easy to stream the fight through pirate sites.

This is why Showtime took some of the most brazen pirate sites to court last week, demanding an injunction to stop the pirated streams before they even start. In its complaint, the cable TV provider listed 44 domain names which advertise the fight, urging the court to shut them down pre-emptively.

A few of the 44 targeted (sub)domains.

After reviewing the application, United States District Judge André Birotte Jr. approved the preliminary injunction, which forbids the site’s operators from offering infringing streams. The injunction stays in place until August 28, two days after the event.

While the order is a clear win for Showtime, it’s unclear how effective it will be. The sites in question are all believed to be connected to LiveStreamHDQ and its alleged operator “Kopa Mayweather,” who Showtime have battled before.

At the time of writing, the sites are all still online, although the language appears to have changed. Many now have articles explaining how the fight can be watched legally. Whether it remains that way has to be seen.

Updated ‘pirate’ site

Interestingly, the injunction doesn’t mention any domain name registrars or registries. When Showtime applied for similar measures in the past, the company specifically asked to take control of domain names, so these couldn’t be used for any infringing activity.

That said, the current order applies to the defendants and any others who are “in active concert or participation” with them, so this might be enough for domain registrars and other parties to take appropriate action.

Showtime also has the possibility to request updates to the injunction, if needed, but with only a few days to go this has to happen swiftly.

As mentioned earlier, this is not the first time that Showtime has gone after alleged pirates before they get a chance to commit an offense. The company launched similar cases for the Mayweather vs. Pacquiao and Mayweather vs. Berto matchups in 2015.

While these efforts were successful in taking a few pirate sites down, there were plenty of unauthorized streams available when the events started. This time it’s not likely to be any different. With hundreds of live streaming sites and tools out there, piracy will remain undefeated.

A copy of the preliminary injunction is available here (pdf).

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

The end of Gentoo’s hardened kernel

Post Syndicated from corbet original https://lwn.net/Articles/731477/rss

Gentoo has long provided a hardened kernel package, but that is
coming to an end
. “As you may know the core of
sys-kernel/hardened-sources has been the grsecurity patches. Recently the
grsecurity developers have decided to limit access to these patches. As a
result, the Gentoo Hardened team is unable to ensure a regular patching
schedule and therefore the security of the users of these kernel
sources. Thus, we will be masking hardened-sources on the 27th of August
and will proceed to remove them from the package repository by the end of

The Windows App Store is Full of Pirate Streaming Apps

Post Syndicated from Ernesto original https://torrentfreak.com/the-windows-app-store-is-full-of-pirate-streaming-apps-170820/

Over the past few years it has become much easier to stream movies and TV-shows over the Internet.

Legal streaming services such as Netflix and Amazon are booming. At the same time, however, there’s also a dark market of thousands of pirate streaming tools.

In recent months, Hollywood has directed many its anti-piracy efforts towards unauthorized Kodi-addons and several popular pirate streaming sites, which offer movies and TV-shows without permission. What seems to be largely ignored, however, is a “store” that hundreds of millions of people have access to; the Windows App Store.

When we were browsing through the “top free” apps in the Windows Store, our attention was drawn to several applications that promoted “free movies” including various Hollywood blockbusters such as “Wonder Woman,” “Spider-Man: Homecoming,” and “The Mummy.”

Initially, we assumed that a pirate app may have slipped passed Microsoft’s screening process. However, the ‘problem’ doesn’t appear to be isolated. There are dozens of similar apps in the official store that promise potential users free movies, most with rave reviews.

Some of the many pirate apps in the “trusted” store

Most of the applications work on multiple platforms including PC, mobile, and the Xbox. They are pretty easy to use and rely on the familiar grid-based streaming interface most sites and services use. Pick a movie or TV-show, click the play button, and off you go.

The sheer number of piracy apps in the Windows Store, using names such as “Free Movies HD,” “Free Movies Online 2020,” and “FreeFlix HQ,” came as a surprise to us. In particular, because the developers make no attempt to hide their activities, quite the opposite.

The app descriptions are littered with colorful language offering the latest Hollywood movies, and thousands of others, without charge. In addition, the apps display their capabilities in various screenshots, including those showing movies that are not yet available on legal streaming platforms.

Screenshot provided by the Windows app store

Making matters worse, the applications show advertising as well, including high-quality pre-roll ads. Some of these appear to be facilitated through Microsoft’s own Ad Monetization platform. Other apps offer paid versions or in-app purchases to monetize their service.

After hours of going through the pirate app offerings, it’s clear that Microsoft’s “trusted” Windows Store is ridden with unauthorized content. Thus far we have only mentioned video, but the issue also applies to pirated music in the form of dedicated streaming and download apps.

Earlier this year, Microsoft signed a landmark anti-piracy agreement with several major copyright holders, to address pirate search results in the Bing search engine. The above makes clear that search results in the Microsoft Store store may require some attention too.

TorrentFreak reached out to Microsoft, asking for a comment on our findings, but at the time of publication we haven’t yet heard back.

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

On ISO standardization of blockchains

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/08/on-iso-standardization-of-blockchains.html

So ISO, the primary international standards organization, is seeking to standardize blockchain technologies. On the surface, this seems a reasonable idea, creating a common standard that everyone can interoperate with.

But it can be silly idea in practice. I mean, it should not be assumed that this is a good thing to do.

The value of official standards

You don’t need the official imprimatur of a government committee for something to be a “standard”. The Internet itself is a prime example of that.

In the 1980s, the ISO and the IETF (Internet Engineering Task Force) pursued competing standards for creating a world-wide “internet”. The IETF was an informal group of technologist that had essentially no official standing.

The ISO version of the Internet failed. Their process was to bring multiple stakeholders from business, government, and universities together in committees to debate competing interests. The result was something so horrible that it could never work in practice.

The IETF succeeded. It consisted of engineers just building things. Rather than officially “standardized”, these things were “described”, so that others knew enough to build their own version that interoperated. Once lots of different people built interoperating versions of something, then it became a “standard”.

In other words, the way the Internet came to be, standardization followed interoperability — it didn’t create interoperability.

In the end, the ISO gave up on their standards and adopted the IETF standards. The ISO brought no value to the development of Internet standards. Whether they ratified the Internet’s “TCP/IP” standard, ignored it, or condemned it, the Internet would exist today anyway, and a competing ISO-blessed internetwork would not.

The same question exists for blockchain technologies. Groups are off busy innovating quickly, creating their own standards. If the ISO blesses one, or creates its own, it’s unlikely to have any impact on interoperability.

Blockchain vs. chaining blocks

The excitement over blockchains is largely driven by people who don’t know the details, who don’t understand the difference between a blockchain like Bitcoin and the problem they are trying to solve.

Consider a record keeping system, especially public records. Storing them in a blockchain seems like a natural idea.

But in fact, it’s a terrible idea. A Bitcoin-style blockchain has a lot of features you don’t want, like “proof-of-work” signing. It is also missing necessary features, like bulk storage with redundancy (backups). Sure, Bitcoin has redundancy, but by brute force, storing the blockchain in thousands of places around the Internet. This is far from what a public records system would need, which would store a lot more data with far fewer backup copies (fewer than 10).

The only real overlap between Bitcoin and a public records system is a “signing chain”. But this is something that already existed before Bitcoin. It’s what Bitcoin blockchain was built on top of — it’s not the blockchain itself.

It’s like people discovering “cryptography” for the first time when they looked at Bitcoin, ignoring the thousand year history of crypto, and now every time they see a need for “crypto” they think “Bitcoin blockchain”.

Consensus and forking

The entire point of Bitcoin, the reason it was created, was as the antithesis to centralized standardization like ISO. Standardizing blockchains misses the entire point of their existence. The Bitcoin manifesto is that standardization comes from acclamation not proclamation, and that many different standards are preferable to a single one.

This is not just a theoretical idea but one built into Bitcoin’s blockchain technology. “Consensus” is achieved by the proof-of-work mechanism, so that those who do the most work are the ones that drive the consensus. When irreconcilable differences arise, the blockchain “forks”, with each side continuing on with their now non-interoperable blockchains. Such forks are not a sin, but part of the natural evolution.

We saw this with the recent fork of Bitcoin. There are now so many transactions that they exceed the size of blocks. One group chose a change to make transactions smaller. Another group chose a change to make block sizes larger.

It is this problem, of consensus, that is the innovation that Bitcoin created with blockchains, not the chain signing of public transaction records.


What “blockchain standardization” is going to mean in practice is not the blockchain itself, but trying to standardize the Ethereum version. What makes Ethereum different is the “smart contracts” programming language, which has financial institutions excited.

This is a bad idea because from a cybersecurity perspective, Ethereum’s programming language is flawed. Different bugs in “smart contracts” have led to multiple $100-million hacks, such as the infamous “DAO collapse”.

While it has interesting possibilities, we should be scared of standardizing Ethereum’s language before it works.


People who matter are too busy innovating, creating their own blockchain standards. There is little that the ISO can do to improve this. Their official imprimatur is not needed to foster innovation and interoperability — if they are consequential at anything, it’ll just be interfering.

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)



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!


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.

New – SES Dedicated IP Pools

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/new-ses-dedicated-ip-pools/

Today we released Dedicated IP Pools for Amazon Simple Email Service (SES). With dedicated IP pools, you can specify which dedicated IP addresses to use for sending different types of email. Dedicated IP pools let you use your SES for different tasks. For instance, you can send transactional emails from one set of IPs and you can send marketing emails from another set of IPs.

If you’re not familiar with Amazon SES these concepts may not make much sense. We haven’t had the chance to cover SES on this blog since 2016, which is a shame, so I want to take a few steps back and talk about the service as a whole and some of the enhancements the team has made over the past year. If you just want the details on this new feature I strongly recommend reading the Amazon Simple Email Service Blog.

What is SES?

So, what is SES? If you’re a customer of Amazon.com you know that we send a lot of emails. Bought something? You get an email. Order shipped? You get an email. Over time, as both email volumes and types increased Amazon.com needed to build an email platform that was flexible, scalable, reliable, and cost-effective. SES is the result of years of Amazon’s own work in dealing with email and maximizing deliverability.

In short: SES gives you the ability to send and receive many types of email with the monitoring and tools to ensure high deliverability.

Sending an email is easy; one simple API call:

import boto3
ses = boto3.client('ses')
    Source='[email protected]',
    Destination={'ToAddresses': ['[email protected]']},
        'Subject': {'Data': 'Hello, World!'},
        'Body': {'Text': {'Data': 'Hello, World!'}}

Receiving and reacting to emails is easy too. You can set up rulesets that forward received emails to Amazon Simple Storage Service (S3), Amazon Simple Notification Service (SNS), or AWS Lambda – you could even trigger a Amazon Lex bot through Lambda to communicate with your customers over email. SES is a powerful tool for building applications. The image below shows just a fraction of the capabilities:

Deliverability 101

Deliverability is the percentage of your emails that arrive in your recipients’ inboxes. Maintaining deliverability is a shared responsibility between AWS and the customer. AWS takes the fight against spam very seriously and works hard to make sure services aren’t abused. To learn more about deliverability I recommend the deliverability docs. For now, understand that deliverability is an important aspect of email campaigns and SES has many tools that enable a customer to manage their deliverability.

Dedicated IPs and Dedicated IP pools

When you’re starting out with SES your emails are sent through a shared IP. That IP is responsible for sending mail on behalf of many customers and AWS works to maintain appropriate volume and deliverability on each of those IPs. However, when you reach a sufficient volume shared IPs may not be the right solution.

By creating a dedicated IP you’re able to fully control the reputations of those IPs. This makes it vastly easier to troubleshoot any deliverability or reputation issues. It’s also useful for many email certification programs which require a dedicated IP as a commitment to maintaining your email reputation. Using the shared IPs of the Amazon SES service is still the right move for many customers but if you have sustained daily sending volume greater than hundreds of thousands of emails per day you might want to consider a dedicated IP. One caveat to be aware of: if you’re not sending a sufficient volume of email with a consistent pattern a dedicated IP can actually hurt your reputation. Dedicated IPs are $24.95 per address per month at the time of this writing – but you can find out more at the pricing page.

Before you can use a Dedicated IP you need to “warm” it. You do this by gradually increasing the volume of emails you send through a new address. Each IP needs time to build a positive reputation. In March of this year SES released the ability to automatically warm your IPs over the course of 45 days. This feature is on by default for all new dedicated IPs.

Customers who send high volumes of email will typically have multiple dedicated IPs. Today the SES team released dedicated IP pools to make managing those IPs easier. Now when you send email you can specify a configuration set which will route your email to an IP in a pool based on the pool’s association with that configuration set.

One of the other major benefits of this feature is that it allows customers who previously split their email sending across several AWS accounts (to manage their reputation for different types of email) to consolidate into a single account.

You can read the documentation and blog for more info.

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 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.

Analyzing AWS Cost and Usage Reports with Looker and Amazon Athena

Post Syndicated from Dillon Morrison original https://aws.amazon.com/blogs/big-data/analyzing-aws-cost-and-usage-reports-with-looker-and-amazon-athena/

This is a guest post by Dillon Morrison at Looker. Looker is, in their own words, “a new kind of analytics platform–letting everyone in your business make better decisions by getting reliable answers from a tool they can use.” 

As the breadth of AWS products and services continues to grow, customers are able to more easily move their technology stack and core infrastructure to AWS. One of the attractive benefits of AWS is the cost savings. Rather than paying upfront capital expenses for large on-premises systems, customers can instead pay variables expenses for on-demand services. To further reduce expenses AWS users can reserve resources for specific periods of time, and automatically scale resources as needed.

The AWS Cost Explorer is great for aggregated reporting. However, conducting analysis on the raw data using the flexibility and power of SQL allows for much richer detail and insight, and can be the better choice for the long term. Thankfully, with the introduction of Amazon Athena, monitoring and managing these costs is now easier than ever.

In the post, I walk through setting up the data pipeline for cost and usage reports, Amazon S3, and Athena, and discuss some of the most common levers for cost savings. I surface tables through Looker, which comes with a host of pre-built data models and dashboards to make analysis of your cost and usage data simple and intuitive.

Analysis with Athena

With Athena, there’s no need to create hundreds of Excel reports, move data around, or deploy clusters to house and process data. Athena uses Apache Hive’s DDL to create tables, and the Presto querying engine to process queries. Analysis can be performed directly on raw data in S3. Conveniently, AWS exports raw cost and usage data directly into a user-specified S3 bucket, making it simple to start querying with Athena quickly. This makes continuous monitoring of costs virtually seamless, since there is no infrastructure to manage. Instead, users can leverage the power of the Athena SQL engine to easily perform ad-hoc analysis and data discovery without needing to set up a data warehouse.

After the data pipeline is established, cost and usage data (the recommended billing data, per AWS documentation) provides a plethora of comprehensive information around usage of AWS services and the associated costs. Whether you need the report segmented by product type, user identity, or region, this report can be cut-and-sliced any number of ways to properly allocate costs for any of your business needs. You can then drill into any specific line item to see even further detail, such as the selected operating system, tenancy, purchase option (on-demand, spot, or reserved), and so on.


By default, the Cost and Usage report exports CSV files, which you can compress using gzip (recommended for performance). There are some additional configuration options for tuning performance further, which are discussed below.


If you want to follow along, you need the following resources:

Enable the cost and usage reports

First, enable the Cost and Usage report. For Time unit, select Hourly. For Include, select Resource IDs. All options are prompted in the report-creation window.

The Cost and Usage report dumps CSV files into the specified S3 bucket. Please note that it can take up to 24 hours for the first file to be delivered after enabling the report.

Configure the S3 bucket and files for Athena querying

In addition to the CSV file, AWS also creates a JSON manifest file for each cost and usage report. Athena requires that all of the files in the S3 bucket are in the same format, so we need to get rid of all these manifest files. If you’re looking to get started with Athena quickly, you can simply go into your S3 bucket and delete the manifest file manually, skip the automation described below, and move on to the next section.

To automate the process of removing the manifest file each time a new report is dumped into S3, which I recommend as you scale, there are a few additional steps. The folks at Concurrency labs wrote a great overview and set of scripts for this, which you can find in their GitHub repo.

These scripts take the data from an input bucket, remove anything unnecessary, and dump it into a new output bucket. We can utilize AWS Lambda to trigger this process whenever new data is dropped into S3, or on a nightly basis, or whatever makes most sense for your use-case, depending on how often you’re querying the data. Please note that enabling the “hourly” report means that data is reported at the hour-level of granularity, not that a new file is generated every hour.

Following these scripts, you’ll notice that we’re adding a date partition field, which isn’t necessary but improves query performance. In addition, converting data from CSV to a columnar format like ORC or Parquet also improves performance. We can automate this process using Lambda whenever new data is dropped in our S3 bucket. Amazon Web Services discusses columnar conversion at length, and provides walkthrough examples, in their documentation.

As a long-term solution, best practice is to use compression, partitioning, and conversion. However, for purposes of this walkthrough, we’re not going to worry about them so we can get up-and-running quicker.

Set up the Athena query engine

In your AWS console, navigate to the Athena service, and click “Get Started”. Follow the tutorial and set up a new database (we’ve called ours “AWS Optimizer” in this example). Don’t worry about configuring your initial table, per the tutorial instructions. We’ll be creating a new table for cost and usage analysis. Once you walked through the tutorial steps, you’ll be able to access the Athena interface, and can begin running Hive DDL statements to create new tables.

One thing that’s important to note, is that the Cost and Usage CSVs also contain the column headers in their first row, meaning that the column headers would be included in the dataset and any queries. For testing and quick set-up, you can remove this line manually from your first few CSV files. Long-term, you’ll want to use a script to programmatically remove this row each time a new file is dropped in S3 (every few hours typically). We’ve drafted up a sample script for ease of reference, which we run on Lambda. We utilize Lambda’s native ability to invoke the script whenever a new object is dropped in S3.

For cost and usage, we recommend using the DDL statement below. Since our data is in CSV format, we don’t need to use a SerDe, we can simply specify the “separatorChar, quoteChar, and escapeChar”, and the structure of the files (“TEXTFILE”). Note that AWS does have an OpenCSV SerDe as well, if you prefer to use that.


identity_LineItemId String,
identity_TimeInterval String,
bill_InvoiceId String,
bill_BillingEntity String,
bill_BillType String,
bill_PayerAccountId String,
bill_BillingPeriodStartDate String,
bill_BillingPeriodEndDate String,
lineItem_UsageAccountId String,
lineItem_LineItemType String,
lineItem_UsageStartDate String,
lineItem_UsageEndDate String,
lineItem_ProductCode String,
lineItem_UsageType String,
lineItem_Operation String,
lineItem_AvailabilityZone String,
lineItem_ResourceId String,
lineItem_UsageAmount String,
lineItem_NormalizationFactor String,
lineItem_NormalizedUsageAmount String,
lineItem_CurrencyCode String,
lineItem_UnblendedRate String,
lineItem_UnblendedCost String,
lineItem_BlendedRate String,
lineItem_BlendedCost String,
lineItem_LineItemDescription String,
lineItem_TaxType String,
product_ProductName String,
product_accountAssistance String,
product_architecturalReview String,
product_architectureSupport String,
product_availability String,
product_bestPractices String,
product_cacheEngine String,
product_caseSeverityresponseTimes String,
product_clockSpeed String,
product_currentGeneration String,
product_customerServiceAndCommunities String,
product_databaseEdition String,
product_databaseEngine String,
product_dedicatedEbsThroughput String,
product_deploymentOption String,
product_description String,
product_durability String,
product_ebsOptimized String,
product_ecu String,
product_endpointType String,
product_engineCode String,
product_enhancedNetworkingSupported String,
product_executionFrequency String,
product_executionLocation String,
product_feeCode String,
product_feeDescription String,
product_freeQueryTypes String,
product_freeTrial String,
product_frequencyMode String,
product_fromLocation String,
product_fromLocationType String,
product_group String,
product_groupDescription String,
product_includedServices String,
product_instanceFamily String,
product_instanceType String,
product_io String,
product_launchSupport String,
product_licenseModel String,
product_location String,
product_locationType String,
product_maxIopsBurstPerformance String,
product_maxIopsvolume String,
product_maxThroughputvolume String,
product_maxVolumeSize String,
product_maximumStorageVolume String,
product_memory String,
product_messageDeliveryFrequency String,
product_messageDeliveryOrder String,
product_minVolumeSize String,
product_minimumStorageVolume String,
product_networkPerformance String,
product_operatingSystem String,
product_operation String,
product_operationsSupport String,
product_physicalProcessor String,
product_preInstalledSw String,
product_proactiveGuidance String,
product_processorArchitecture String,
product_processorFeatures String,
product_productFamily String,
product_programmaticCaseManagement String,
product_provisioned String,
product_queueType String,
product_requestDescription String,
product_requestType String,
product_routingTarget String,
product_routingType String,
product_servicecode String,
product_sku String,
product_softwareType String,
product_storage String,
product_storageClass String,
product_storageMedia String,
product_technicalSupport String,
product_tenancy String,
product_thirdpartySoftwareSupport String,
product_toLocation String,
product_toLocationType String,
product_training String,
product_transferType String,
product_usageFamily String,
product_usagetype String,
product_vcpu String,
product_version String,
product_volumeType String,
product_whoCanOpenCases String,
pricing_LeaseContractLength String,
pricing_OfferingClass String,
pricing_PurchaseOption String,
pricing_publicOnDemandCost String,
pricing_publicOnDemandRate String,
pricing_term String,
pricing_unit String,
reservation_AvailabilityZone String,
reservation_NormalizedUnitsPerReservation String,
reservation_NumberOfReservations String,
reservation_ReservationARN String,
reservation_TotalReservedNormalizedUnits String,
reservation_TotalReservedUnits String,
reservation_UnitsPerReservation String,
resourceTags_userName String,
resourceTags_usercostcategory String  

      ESCAPED BY '\\'

    LOCATION 's3://<<your bucket name>>';

Once you’ve successfully executed the command, you should see a new table named “cost_and_usage” with the below properties. Now we’re ready to start executing queries and running analysis!

Start with Looker and connect to Athena

Setting up Looker is a quick process, and you can try it out for free here (or download from Amazon Marketplace). It takes just a few seconds to connect Looker to your Athena database, and Looker comes with a host of pre-built data models and dashboards to make analysis of your cost and usage data simple and intuitive. After you’re connected, you can use the Looker UI to run whatever analysis you’d like. Looker translates this UI to optimized SQL, so any user can execute and visualize queries for true self-service analytics.

Major cost saving levers

Now that the data pipeline is configured, you can dive into the most popular use cases for cost savings. In this post, I focus on:

  • Purchasing Reserved Instances vs. On-Demand Instances
  • Data transfer costs
  • Allocating costs over users or other Attributes (denoted with resource tags)

On-Demand, Spot, and Reserved Instances

Purchasing Reserved Instances vs On-Demand Instances is arguably going to be the biggest cost lever for heavy AWS users (Reserved Instances run up to 75% cheaper!). AWS offers three options for purchasing instances:

  • On-Demand—Pay as you use.
  • Spot (variable cost)—Bid on spare Amazon EC2 computing capacity.
  • Reserved Instances—Pay for an instance for a specific, allotted period of time.

When purchasing a Reserved Instance, you can also choose to pay all-upfront, partial-upfront, or monthly. The more you pay upfront, the greater the discount.

If your company has been using AWS for some time now, you should have a good sense of your overall instance usage on a per-month or per-day basis. Rather than paying for these instances On-Demand, you should try to forecast the number of instances you’ll need, and reserve them with upfront payments.

The total amount of usage with Reserved Instances versus overall usage with all instances is called your coverage ratio. It’s important not to confuse your coverage ratio with your Reserved Instance utilization. Utilization represents the amount of reserved hours that were actually used. Don’t worry about exceeding capacity, you can still set up Auto Scaling preferences so that more instances get added whenever your coverage or utilization crosses a certain threshold (we often see a target of 80% for both coverage and utilization among savvy customers).

Calculating the reserved costs and coverage can be a bit tricky with the level of granularity provided by the cost and usage report. The following query shows your total cost over the last 6 months, broken out by Reserved Instance vs other instance usage. You can substitute the cost field for usage if you’d prefer. Please note that you should only have data for the time period after the cost and usage report has been enabled (though you can opt for up to 3 months of historical data by contacting your AWS Account Executive). If you’re just getting started, this query will only show a few days.


	DATE_FORMAT(from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate),'%Y-%m') AS "cost_and_usage.usage_start_month",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0) AS "cost_and_usage.total_unblended_cost",
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_reserved_unblended_cost",
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_on_ris",
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_non_reserved_unblended_cost",
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_on_non_ris"
FROM aws_optimizer.cost_and_usage  AS cost_and_usage

	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))

The resulting table should look something like the image below (I’m surfacing tables through Looker, though the same table would result from querying via command line or any other interface).

With a BI tool, you can create dashboards for easy reference and monitoring. New data is dumped into S3 every few hours, so your dashboards can update several times per day.

It’s an iterative process to understand the appropriate number of Reserved Instances needed to meet your business needs. After you’ve properly integrated Reserved Instances into your purchasing patterns, the savings can be significant. If your coverage is consistently below 70%, you should seriously consider adjusting your purchase types and opting for more Reserved instances.

Data transfer costs

One of the great things about AWS data storage is that it’s incredibly cheap. Most charges often come from moving and processing that data. There are several different prices for transferring data, broken out largely by transfers between regions and availability zones. Transfers between regions are the most costly, followed by transfers between Availability Zones. Transfers within the same region and same availability zone are free unless using elastic or public IP addresses, in which case there is a cost. You can find more detailed information in the AWS Pricing Docs. With this in mind, there are several simple strategies for helping reduce costs.

First, since costs increase when transferring data between regions, it’s wise to ensure that as many services as possible reside within the same region. The more you can localize services to one specific region, the lower your costs will be.

Second, you should maximize the data you’re routing directly within AWS services and IP addresses. Transfers out to the open internet are the most costly and least performant mechanisms of data transfers, so it’s best to keep transfers within AWS services.

Lastly, data transfers between private IP addresses are cheaper than between elastic or public IP addresses, so utilizing private IP addresses as much as possible is the most cost-effective strategy.

The following query provides a table depicting the total costs for each AWS product, broken out transfer cost type. Substitute the “lineitem_productcode” field in the query to segment the costs by any other attribute. If you notice any unusually high spikes in cost, you’ll need to dig deeper to understand what’s driving that spike: location, volume, and so on. Drill down into specific costs by including “product_usagetype” and “product_transfertype” in your query to identify the types of transfer costs that are driving up your bill.

	cost_and_usage.lineitem_productcode  AS "cost_and_usage.product_code",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost), 0) AS "cost_and_usage.total_unblended_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_data_transfer_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer-In')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_inbound_data_transfer_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer-Out')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_outbound_data_transfer_cost"
FROM aws_optimizer.cost_and_usage  AS cost_and_usage

	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))

When moving between regions or over the open web, many data transfer costs also include the origin and destination location of the data movement. Using a BI tool with mapping capabilities, you can get a nice visual of data flows. The point at the center of the map is used to represent external data flows over the open internet.

Analysis by tags

AWS provides the option to apply custom tags to individual resources, so you can allocate costs over whatever customized segment makes the most sense for your business. For a SaaS company that hosts software for customers on AWS, maybe you’d want to tag the size of each customer. The following query uses custom tags to display the reserved, data transfer, and total cost for each AWS service, broken out by tag categories, over the last 6 months. You’ll want to substitute the cost_and_usage.resourcetags_customersegment and cost_and_usage.customer_segment with the name of your customer field.


SELECT *, DENSE_RANK() OVER (ORDER BY z___min_rank) as z___pivot_row_rank, RANK() OVER (PARTITION BY z__pivot_col_rank ORDER BY z___min_rank) as z__pivot_col_ordering FROM (
SELECT *, MIN(z___rank) OVER (PARTITION BY "cost_and_usage.product_code") as z___min_rank FROM (
SELECT *, RANK() OVER (ORDER BY CASE WHEN z__pivot_col_rank=1 THEN (CASE WHEN "cost_and_usage.total_unblended_cost" IS NOT NULL THEN 0 ELSE 1 END) ELSE 2 END, CASE WHEN z__pivot_col_rank=1 THEN "cost_and_usage.total_unblended_cost" ELSE NULL END DESC, "cost_and_usage.total_unblended_cost" DESC, z__pivot_col_rank, "cost_and_usage.product_code") AS z___rank FROM (
SELECT *, DENSE_RANK() OVER (ORDER BY CASE WHEN "cost_and_usage.customer_segment" IS NULL THEN 1 ELSE 0 END, "cost_and_usage.customer_segment") AS z__pivot_col_rank FROM (
	cost_and_usage.lineitem_productcode  AS "cost_and_usage.product_code",
	cost_and_usage.resourcetags_customersegment  AS "cost_and_usage.customer_segment",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0) AS "cost_and_usage.total_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_data_transfers_unblended",
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.unblended_percent_spend_on_ris"
FROM aws_optimizer.cost_and_usage_raw  AS cost_and_usage

	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1,2) ww
) bb WHERE z__pivot_col_rank <= 16384
) aa
) xx
) zz
 WHERE z___pivot_row_rank <= 500 OR z__pivot_col_ordering = 1 ORDER BY z___pivot_row_rank

The resulting table in this example looks like the results below. In this example, you can tell that we’re making poor use of Reserved Instances because they represent such a small portion of our overall costs.

Again, using a BI tool to visualize these costs and trends over time makes the analysis much easier to consume and take action on.


Saving costs on your AWS spend is always an iterative, ongoing process. Hopefully with these queries alone, you can start to understand your spending patterns and identify opportunities for savings. However, this is just a peek into the many opportunities available through analysis of the Cost and Usage report. Each company is different, with unique needs and usage patterns. To achieve maximum cost savings, we encourage you to set up an analytics environment that enables your team to explore all potential cuts and slices of your usage data, whenever it’s necessary. Exploring different trends and spikes across regions, services, user types, etc. helps you gain comprehensive understanding of your major cost levers and consistently implement new cost reduction strategies.

Note that all of the queries and analysis provided in this post were generated using the Looker data platform. If you’re already a Looker customer, you can get all of this analysis, additional pre-configured dashboards, and much more using Looker Blocks for AWS.

About the Author

Dillon Morrison leads the Platform Ecosystem at Looker. He enjoys exploring new technologies and architecting the most efficient data solutions for the business needs of his company and their customers. In his spare time, you’ll find Dillon rock climbing in the Bay Area or nose deep in the docs of the latest AWS product release at his favorite cafe (“Arlequin in SF is unbeatable!”).