Tag Archives: BP

Court Grants Subpoenas to Unmask ‘TVAddons’ and ‘ZemTV’ Operators

Post Syndicated from Ernesto original https://torrentfreak.com/court-grants-subpoenas-to-unmask-tvaddons-and-zemtv-operators-170621/

Earlier this month we broke the news that third-party Kodi add-on ZemTV and the TVAddons library were being sued in a federal court in Texas.

In a complaint filed by American satellite and broadcast provider Dish Network, both stand accused of copyright infringement, facing up to $150,000 for each offense.

While the allegations are serious, Dish doesn’t know the full identities of the defendants.

To find out more, the company requested a broad range of subpoenas from the court, targeting Amazon, Github, Google, Twitter, Facebook, PayPal, and several hosting providers.

From Dish’s request

This week the court granted the subpoenas, which means that they can be forwarded to the companies in question. Whether that will be enough to identify the people behind ‘TVAddons’ and ‘ZemTV’ remains to be seen, but Dish has cast its net wide.

For example, the subpoena directed at Google covers any type of information that can be used to identify the account holder of taacc14@gmail.com, which is believed to be tied to ZemTV.

The information requested from Google includes IP address logs with session date and timestamps, but also covers “all communications,” including GChat messages from 2014 onwards.

Similarly, Twitter is required to hand over information tied to the accounts of the users “TV Addons” and “shani_08_kodi” as well as other accounts linked to tvaddons.ag and streamingboxes.com. This also applies the various tweets that were sent through the account.

The subpoena specifically mentions “all communications, including ‘tweets’, Twitter sent to or received from each Twitter Account during the time period of February 1, 2014 to present.”

From the Twitter subpoena

Similar subpoenas were granted for the other services, tailored towards the information Dish hopes to find there. For example, the broadcast provider also requests details of each transaction from PayPal, as well as all debits and credits to the accounts.

In some parts, the subpoenas appear to be quite broad. PayPal is asked to reveal information on any account with the credit card statement “Shani,” for example. Similarly, Github is required to hand over information on accounts that are ‘associated’ with the tvaddons.ag domain, which is referenced by many people who are not directly connected to the site.

The service providers in question still have the option to challenge the subpoenas or ask the court for further clarification. A full overview of all the subpoena requests is available here (Exhibit 2 and onwards), including all the relevant details. This also includes several letters to foreign hosting providers.

While Dish still appears to be keen to find out who is behind ‘TVAddons’ and ‘ZemTV,’ not much has been heard from the defendants in question.

ZemTV developer “Shani” shut down his addon soon after the lawsuit was announced, without mentioning it specifically. TVAddons, meanwhile, has been offline for well over a week, without any notice in public about the reason for the prolonged downtime.

The court’s order granting the subpoenas and letters of request is available here (pdf).

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

BPI Breaks Record After Sending 310 Million Google Takedowns

Post Syndicated from Andy original https://torrentfreak.com/bpi-breaks-record-after-sending-310-million-google-takedowns-170619/

A little over a year ago during March 2016, music industry group BPI reached an important milestone. After years of sending takedown notices to Google, the group burst through the 200 million URL barrier.

The fact that it took BPI several years to reach its 200 million milestone made the surpassing of the quarter billion milestone a few months later even more remarkable. In October 2016, the group sent its 250 millionth takedown to Google, a figure that nearly doubled when accounting for notices sent to Microsoft’s Bing.

But despite the volumes, the battle hadn’t been won, let alone the war. The BPI’s takedown machine continued to run at a remarkable rate, churning out millions more notices per week.

As a result, yet another new milestone was reached this month when the BPI smashed through the 300 million URL barrier. Then, days later, a further 10 million were added, with the latter couple of million added during the time it took to put this piece together.

BPI takedown notices, as reported by Google

While demanding that Google places greater emphasis on its de-ranking of ‘pirate’ sites, the BPI has called again and again for a “notice and stay down” regime, to ensure that content taken down by the search engine doesn’t simply reappear under a new URL. It’s a position BPI maintains today.

“The battle would be a whole lot easier if intermediaries played fair,” a BPI spokesperson informs TF.

“They need to take more proactive responsibility to reduce infringing content that appears on their platform, and, where we expressly notify infringing content to them, to ensure that they do not only take it down, but also keep it down.”

The long-standing suggestion is that the volume of takedown notices sent would reduce if a “take down, stay down” regime was implemented. The BPI says it’s difficult to present a precise figure but infringing content has a tendency to reappear, both in search engines and on hosting sites.

“Google rejects repeat notices for the same URL. But illegal content reappears as it is re-indexed by Google. As to the sites that actually host the content, the vast majority of notices sent to them could be avoided if they implemented take-down & stay-down,” BPI says.

The fact that the BPI has added 60 million more takedowns since the quarter billion milestone a few months ago is quite remarkable, particularly since there appears to be little slowdown from month to month. However, the numbers have grown so huge that 310 billion now feels a lot like 250 million, with just a few added on top for good measure.

That an extra 60 million takedowns can almost be dismissed as a handful is an indication of just how massive the issue is online. While pirates always welcome an abundance of links to juicy content, it’s no surprise that groups like the BPI are seeking more comprehensive and sustainable solutions.

Previously, it was hoped that the Digital Economy Bill would provide some relief, hopefully via government intervention and the imposition of a search engine Code of Practice. In the event, however, all pressure on search engines was removed from the legislation after a separate voluntary agreement was reached.

All parties agreed that the voluntary code should come into effect two weeks ago on June 1 so it seems likely that some effects should be noticeable in the near future. But the BPI says it’s still early days and there’s more work to be done.

“BPI has been working productively with search engines since the voluntary code was agreed to understand how search engines approach the problem, but also what changes can and have been made and how results can be improved,” the group explains.

“The first stage is to benchmark where we are and to assess the impact of the changes search engines have made so far. This will hopefully be completed soon, then we will have better information of the current picture and from that we hope to work together to continue to improve search for rights owners and consumers.”

With more takedown notices in the pipeline not yet publicly reported by Google, the BPI informs TF that it has now notified the search giant of 315 million links to illegal content.

“That’s an astonishing number. More than 1 in 10 of the entire world’s notices to Google come from BPI. This year alone, one in every three notices sent to Google from BPI is for independent record label repertoire,” BPI concludes.

While it’s clear that groups like BPI have developed systems to cope with the huge numbers of takedown notices required in today’s environment, it’s clear that few rightsholders are happy with the status quo. With that in mind, the fight will continue, until search engines are forced into compromise. Considering the implications, that could only appear on a very distant horizon.

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

More notes on US-CERTs IOCs

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/06/more-notes-on-us-certs-iocs.html

Yet another Russian attack against the power grid, and yet more bad IOCs from the DHS US-CERT.

IOCs are “indicators of compromise“, things you can look for in order to order to see if you, too, have been hacked by the same perpetrators. There are several types of IOCs, ranging from the highly specific to the uselessly generic.

A uselessly generic IOC would be like trying to identify bank robbers by the fact that their getaway car was “white” in color. It’s worth documenting, so that if the police ever show up in a suspected cabin in the woods, they can note that there’s a “white” car parked in front.

But if you work bank security, that doesn’t mean you should be on the lookout for “white” cars. That would be silly.

This is what happens with US-CERT’s IOCs. They list some potentially useful things, but they also list a lot of junk that waste’s people’s times, with little ability to distinguish between the useful and the useless.

An example: a few months ago was the GRIZZLEYBEAR report published by US-CERT. Among other things, it listed IP addresses used by hackers. There was no description which would be useful IP addresses to watch for, and which would be useless.

Some of these IP addresses were useful, pointing to servers the group has been using a long time as command-and-control servers. Other IP addresses are more dubious, such as Tor exit nodes. You aren’t concerned about any specific Tor exit IP address, because it changes randomly, so has no relationship to the attackers. Instead, if you cared about those Tor IP addresses, what you should be looking for is a dynamically updated list of Tor nodes updated daily.

And finally, they listed IP addresses of Yahoo, because attackers passed data through Yahoo servers. No, it wasn’t because those Yahoo servers had been compromised, it’s just that everyone passes things though them, like email.

A Vermont power-plant blindly dumped all those IP addresses into their sensors. As a consequence, the next morning when an employee checked their Yahoo email, the sensors triggered. This resulted in national headlines about the Russians hacking the Vermont power grid.

Today, the US-CERT made similar mistakes with CRASHOVERRIDE. They took a report from Dragos Security, then mutilated it. Dragos’s own IOCs focused on things like hostile strings and file hashes of the hostile files. They also included filenames, but similar to the reason you’d noticed a white car — because it happened, not because you should be on the lookout for it. In context, there’s nothing wrong with noting the file name.

But the US-CERT pulled the filenames out of context. One of those filenames was, humorously, “svchost.exe”. It’s the name of an essential Windows service. Every Windows computer is running multiple copies of “svchost.exe”. It’s like saying “be on the lookout for Windows”.

Yes, it’s true that viruses use the same filenames as essential Windows files like “svchost.exe”. That’s, generally, something you should be aware of. But that CRASHOVERRIDE did this is wholly meaningless.

What Dragos Security was actually reporting was that a “svchost.exe” with the file hash of 79ca89711cdaedb16b0ccccfdcfbd6aa7e57120a was the virus — it’s the hash that’s the important IOC. Pulling the filename out of context is just silly.

Luckily, the DHS also provides some of the raw information provided by Dragos. But even then, there’s problems: they provide it in formatted form, for HTML, PDF, or Excel documents. This corrupts the original data so that it’s no longer machine readable. For example, from their webpage, they have the following:

import “pe”
import “hash”

Among the problems are the fact that the quote marks have been altered, probably by Word’s “smart quotes” feature. In other cases, I’ve seen PDF documents get confused by the number 0 and the letter O, as if the raw data had been scanned in from a printed document and OCRed.

If this were a “threat intel” company,  we’d call this snake oil. The US-CERT is using Dragos Security’s reports to promote itself, but ultimate providing negative value, mutilating the content.

This, ultimately, causes a lot of harm. The press trusted their content. So does the network of downstream entities, like municipal power grids. There are tens of thousands of such consumers of these reports, often with less expertise than even US-CERT. There are sprinklings of smart people in these organizations, I meet them at hacker cons, and am fascinated by their stories. But institutionally, they are dumbed down the same level as these US-CERT reports, with the smart people marginalized.

There are two solutions to this problem. The first is that when the stupidity of what you do causes everyone to laugh at you, stop doing it. The second is to value technical expertise, empowering those who know what they are doing. Examples of what not to do are giving power to people like Obama’s cyberczar, Michael Daniels, who once claimed his lack of technical knowledge was a bonus, because it allowed him to see the strategic picture instead of getting distracted by details.

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/724795/rss

Security updates have been issued by Arch Linux (chromium), Debian (apng2gif and ming), Gentoo (freetype, libpcre, minicom, pidgin, webkit-gtk, and wireshark), openSUSE (deluge and postgresql93), and Ubuntu (libnl3, lintian, linux, linux-aws, linux-gke, linux-raspi2, linux-snapdragon, linux, linux-raspi2, linux-hwe, and linux-lts-xenial).

FUNimation Targets ‘Pirate’ Streaming Site KissAnime

Post Syndicated from Ernesto original https://torrentfreak.com/funimation-targets-pirate-streaming-site-kissanime-170601/

American anime distributor FUNimation is no stranger to hunting down pirates.

Headquartered in Texas, the company targeted 1337 alleged BitTorrent downloaders of the anime series “One Piece” at a local court a few years ago.

While the company no longer targets individual users through the U.S. legal system, it now appears to have its eyes set on a higher profile target, the popular anime streaming site KissAnime.

With millions of pageviews per day, KissAnime is the go-to site for many anime fans. The site is listed among the 250 most visited websites in the United States, making it one of the largest unauthorized streaming platforms in the world.

This is a thorn in the side of FUNimation, which recently obtained a DMCA subpoena to unmask part of the site’s infrastructure. Like many other streaming portals, KissAnime uses Google’s servers to host videos. These videos are served through CDN links, presumably to make them harder to take down.

FUNimation traced a CDN IP-address, used by KissAnime to stream pirated “One Piece” content, back to U.S. cloud hosting platform DigitalOcean, and asked the company to disable the associated link.

“Through our investigations, we have a good faith belief that a web server for which Digital Ocean, Inc. provides service, located at 138.68.244.174, is being used for the unauthorized copying and distribution […] of digital files embodying the Property,” FUNimation lawyer Evan Stone recently wrote to the company.

“FUNimation hereby requests that Digital Ocean expeditiously causes all such infringing materials to be removed or blocked or freezes the account at issue until the account holder removes all infringing materials or disables access thereto.”

FUNimation DMCA notice sent to Digital Ocean

Although KissAnime isn’t specifically mentioned in the DMCA notice or the subpoena request, a source close to the issue informs TorrentFreak that the IP-address in question is linked to the anime streaming site.

Because the CDN links keep rotating, FUNimation now wants to know the name of the customer that’s connected to the IP-address in question. The company therefore requested a DMCA subpoena from a federal court in Texas, which was granted earlier this month.

The subpoena orders DigitalOcean to hand over any and all contact information they have on the customer linked to the offending IP-address.

The DMCA subpoena

To find out what FUNimation intends to do with the information, provided that DigitalOcean will hand it over, we contacted the company’s lawyer Evan Stone. He couldn’t confirm the target but noted that it’s not about an end-user.

“We are targeting someone associated with disseminating infringing content on a MASSIVE scale, for profit. This is not a prelude to an end-user lawsuit, nor does this involve your typical fan uploader,” Stone told TF.

It’s likely that Funimation will pursue further action against the DigitalOcean customer associated with the pirates KissAnime streams. Whether this will be a central player or someone only remotely connected to the site remains unknown for now.

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

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/724293/rss

Security updates have been issued by Arch Linux (postgresql, postgresql-libs, samba, and sudo), Debian (gajim, libpodofo, openldap, pngquant, qemu-kvm, sudo, and tiff), Fedora (lxterminal, menu-cache, and pcmanfm), Gentoo (sudo), openSUSE (libraw, miniupnpc, and sudo), Oracle (kernel, nss, and sudo), Red Hat (kernel and sudo), Scientific Linux (kernel and sudo), Slackware (sudo), SUSE (java-1_6_0-ibm, java-1_8_0-openjdk, openstack-components, and sudo), and Ubuntu (sudo).

Security updates for Tuesday

Post Syndicated from ris original https://lwn.net/Articles/724150/rss

Security updates have been issued by Arch Linux (lib32-nss), Debian (bind9, exiv2, fop, imagemagick, libical, libonig, libsndfile, mosquitto, openjdk-7, rzip, strongswan, and tnef), Fedora (git, kernel, lynis, moodle, mupdf, samba, systemd, and webkitgtk4), Mageia (perl-Image-Info and vlc), openSUSE (ffmpeg2, git, java-1_7_0-openjdk, libplist, libsndfile, and samba), Oracle (kernel and samba3x), Red Hat (nss), Scientific Linux (nss), and Ubuntu (imagemagick, juju-core, libtiff, strongswan, and webkit2gtk).

Build a Serverless Architecture to Analyze Amazon CloudFront Access Logs Using AWS Lambda, Amazon Athena, and Amazon Kinesis Analytics

Post Syndicated from Rajeev Srinivasan original https://aws.amazon.com/blogs/big-data/build-a-serverless-architecture-to-analyze-amazon-cloudfront-access-logs-using-aws-lambda-amazon-athena-and-amazon-kinesis-analytics/

Nowadays, it’s common for a web server to be fronted by a global content delivery service, like Amazon CloudFront. This type of front end accelerates delivery of websites, APIs, media content, and other web assets to provide a better experience to users across the globe.

The insights gained by analysis of Amazon CloudFront access logs helps improve website availability through bot detection and mitigation, optimizing web content based on the devices and browser used to view your webpages, reducing perceived latency by caching of popular object closer to its viewer, and so on. This results in a significant improvement in the overall perceived experience for the user.

This blog post provides a way to build a serverless architecture to generate some of these insights. To do so, we analyze Amazon CloudFront access logs both at rest and in transit through the stream. This serverless architecture uses Amazon Athena to analyze large volumes of CloudFront access logs (on the scale of terabytes per day), and Amazon Kinesis Analytics for streaming analysis.

The analytic queries in this blog post focus on three common use cases:

  1. Detection of common bots using the user agent string
  2. Calculation of current bandwidth usage per Amazon CloudFront distribution per edge location
  3. Determination of the current top 50 viewers

However, you can easily extend the architecture described to power dashboards for monitoring, reporting, and trigger alarms based on deeper insights gained by processing and analyzing the logs. Some examples are dashboards for cache performance, usage and viewer patterns, and so on.

Following we show a diagram of this architecture.

Prerequisites

Before you set up this architecture, install the AWS Command Line Interface (AWS CLI) tool on your local machine, if you don’t have it already.

Setup summary

The following steps are involved in setting up the serverless architecture on the AWS platform:

  1. Create an Amazon S3 bucket for your Amazon CloudFront access logs to be delivered to and stored in.
  2. Create a second Amazon S3 bucket to receive processed logs and store the partitioned data for interactive analysis.
  3. Create an Amazon Kinesis Firehose delivery stream to batch, compress, and deliver the preprocessed logs for analysis.
  4. Create an AWS Lambda function to preprocess the logs for analysis.
  5. Configure Amazon S3 event notification on the CloudFront access logs bucket, which contains the raw logs, to trigger the Lambda preprocessing function.
  6. Create an Amazon DynamoDB table to look up partition details, such as partition specification and partition location.
  7. Create an Amazon Athena table for interactive analysis.
  8. Create a second AWS Lambda function to add new partitions to the Athena table based on the log delivered to the processed logs bucket.
  9. Configure Amazon S3 event notification on the processed logs bucket to trigger the Lambda partitioning function.
  10. Configure Amazon Kinesis Analytics application for analysis of the logs directly from the stream.

ETL and preprocessing

In this section, we parse the CloudFront access logs as they are delivered, which occurs multiple times in an hour. We filter out commented records and use the user agent string to decipher the browser name, the name of the operating system, and whether the request has been made by a bot. For more details on how to decipher the preceding information based on the user agent string, see user-agents 1.1.0 in the Python documentation.

We use the Lambda preprocessing function to perform these tasks on individual rows of the access log. On successful completion, the rows are pushed to an Amazon Kinesis Firehose delivery stream to be persistently stored in an Amazon S3 bucket, the processed logs bucket.

To create a Firehose delivery stream with a new or existing S3 bucket as the destination, follow the steps described in Create a Firehose Delivery Stream to Amazon S3 in the S3 documentation. Keep most of the default settings, but select an AWS Identity and Access Management (IAM) role that has write access to your S3 bucket and specify GZIP compression. Name the delivery stream CloudFrontLogsToS3.

Another pre-requisite for this setup is to create an IAM role that provides the necessary permissions our AWS Lambda function to get the data from S3, process it, and deliver it to the CloudFrontLogsToS3 delivery stream.

Let’s use the AWS CLI to create the IAM role using the following the steps:

  1. Create the IAM policy (lambda-exec-policy) for the Lambda execution role to use.
  2. Create the Lambda execution role (lambda-cflogs-exec-role) and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

To download the policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/preprocessiong-lambda/lambda-exec-policy.json  <path_on_your_local_machine>

To download the assume policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/preprocessiong-lambda/assume-lambda-policy.json  <path_on_your_local_machine>

Following is the lambda-exec-policy.json file, which is the IAM policy used by the Lambda execution role.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "CloudWatchAccess",
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "arn:aws:logs:*:*:*"
        },
        {
            "Sid": "S3Access",
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::*"
            ]
        },
        {
            "Sid": "FirehoseAccess",
            "Effect": "Allow",
            "Action": [
                "firehose:ListDeliveryStreams",
                "firehose:PutRecord",
                "firehose:PutRecordBatch"
            ],
            "Resource": [
                "arn:aws:firehose:*:*:deliverystream/CloudFrontLogsToS3"
            ]
        }
    ]
}

To create the IAM policy used by Lambda execution role, type the following command.

aws iam create-policy --policy-name lambda-exec-policy --policy-document file://<path>/lambda-exec-policy.json

To create the AWS Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-cflogs-exec-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

Following is the assume-lambda-policy.json file, to grant Lambda permission to assume a role.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

To attach the policy (lambda-exec-policy) created to the AWS Lambda execution role (lambda-cflogs-exec-role), type the following command.

aws iam attach-role-policy --role-name lambda-cflogs-exec-role --policy-arn arn:aws:iam::<your-account-id>:policy/lambda-exec-policy

Now that we have created the CloudFrontLogsToS3 Firehose delivery stream and the lambda-cflogs-exec-role IAM role for Lambda, the next step is to create a Lambda preprocessing function.

This Lambda preprocessing function parses the CloudFront access logs delivered into the S3 bucket and performs a few transformation and mapping operations on the data. The Lambda function adds descriptive information, such as the browser and the operating system that were used to make this request based on the user agent string found in the logs. The Lambda function also adds information about the web distribution to support scenarios where CloudFront access logs are delivered to a centralized S3 bucket from multiple distributions. With the solution in this blog post, you can get insights across distributions and their edge locations.

Use the Lambda Management Console to create a new Lambda function with a Python 2.7 runtime and the s3-get-object-python blueprint. Open the console, and on the Configure triggers page, choose the name of the S3 bucket where the CloudFront access logs are delivered. Choose Put for Event type. For Prefix, type the name of the prefix, if any, for the folder where CloudFront access logs are delivered, for example cloudfront-logs/. To invoke Lambda to retrieve the logs from the S3 bucket as they are delivered, select Enable trigger.

Choose Next and provide a function name to identify this Lambda preprocessing function.

For Code entry type, choose Upload a file from Amazon S3. For S3 link URL, type https.amazonaws.com//preprocessing-lambda/pre-data.zip. In the section, also create an environment variable with the key KINESIS_FIREHOSE_STREAM and a value with the name of the Firehose delivery stream as CloudFrontLogsToS3.

Choose lambda-cflogs-exec-role as the IAM role for the Lambda function, and type prep-data.lambda_handler for the value for Handler.

Choose Next, and then choose Create Lambda.

Table creation in Amazon Athena

In this step, we will build the Athena table. Use the Athena console in the same region and create the table using the query editor.

CREATE EXTERNAL TABLE IF NOT EXISTS cf_logs (
  logdate date,
  logtime string,
  location string,
  bytes bigint,
  requestip string,
  method string,
  host string,
  uri string,
  status bigint,
  referrer string,
  useragent string,
  uriquery string,
  cookie string,
  resulttype string,
  requestid string,
  header string,
  csprotocol string,
  csbytes string,
  timetaken bigint,
  forwardedfor string,
  sslprotocol string,
  sslcipher string,
  responseresulttype string,
  protocolversion string,
  browserfamily string,
  osfamily string,
  isbot string,
  filename string,
  distribution string
)
PARTITIONED BY(year string, month string, day string, hour string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
LOCATION 's3://<pre-processing-log-bucket>/prefix/';

Creation of the Athena partition

A popular website with millions of requests each day routed using Amazon CloudFront can generate a large volume of logs, on the order of a few terabytes a day. We strongly recommend that you partition your data to effectively restrict the amount of data scanned by each query. Partitioning significantly improves query performance and substantially reduces cost. The Lambda partitioning function adds the partition information to the Athena table for the data delivered to the preprocessed logs bucket.

Before delivering the preprocessed Amazon CloudFront logs file into the preprocessed logs bucket, Amazon Kinesis Firehose adds a UTC time prefix in the format YYYY/MM/DD/HH. This approach supports multilevel partitioning of the data by year, month, date, and hour. You can invoke the Lambda partitioning function every time a new processed Amazon CloudFront log is delivered to the preprocessed logs bucket. To do so, configure the Lambda partitioning function to be triggered by an S3 Put event.

For a website with millions of requests, a large number of preprocessed logs can be delivered multiple times in an hour—for example, at the interval of one each second. To avoid querying the Athena table for partition information every time a preprocessed log file is delivered, you can create an Amazon DynamoDB table for fast lookup.

Based on the year, month, data and hour in the prefix of the delivered log, the Lambda partitioning function checks if the partition specification exists in the Amazon DynamoDB table. If it doesn’t, it’s added to the table using an atomic operation, and then the Athena table is updated.

Type the following command to create the Amazon DynamoDB table.

aws dynamodb create-table --table-name athenapartitiondetails \
--attribute-definitions AttributeName=PartitionSpec,AttributeType=S \
--key-schema AttributeName=PartitionSpec,KeyType=HASH \
--provisioned-throughput ReadCapacityUnits=100,WriteCapacityUnits=100

Here the following is true:

  • PartitionSpec is the hash key and is a representation of the partition signature—for example, year=”2017”; month=”05”; day=”15”; hour=”10”.
  • Depending on the rate at which the processed log files are delivered to the processed log bucket, you might have to increase the ReadCapacityUnits and WriteCapacityUnits values, if these are throttled.

The other attributes besides PartitionSpec are the following:

  • PartitionPath – The S3 path associated with the partition.
  • PartitionType – The type of partition used (Hour, Month, Date, Year, or ALL). In this case, ALL is used.

Next step is to create the IAM role to provide permissions for the Lambda partitioning function. You require permissions to do the following:

  1. Look up and write partition information to DynamoDB.
  2. Alter the Athena table with new partition information.
  3. Perform Amazon CloudWatch logs operations.
  4. Perform Amazon S3 operations.

To download the policy document to your local machine, type following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/lambda-partition-function-execution-policy.json  <path_on_your_local_machine>

To download the assume policy document to your local machine, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/assume-lambda-policy.json <path_on_your_local_machine>

To create the Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-cflogs-exec-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

Let’s use the AWS CLI to create the IAM role using the following three steps:

  1. Create the IAM policy(lambda-partition-exec-policy) used by the Lambda execution role.
  2. Create the Lambda execution role (lambda-partition-execution-role)and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

To create the IAM policy used by Lambda execution role, type the following command.

aws iam create-policy --policy-name lambda-partition-exec-policy --policy-document file://<path>/lambda-partition-function-execution-policy.json

To create the Lambda execution role and assign the service to use this role, type the following command.

aws iam create-role --role-name lambda-partition-execution-role --assume-role-policy-document file://<path>/assume-lambda-policy.json

To attach the policy (lambda-partition-exec-policy) created to the AWS Lambda execution role (lambda-partition-execution-role), type the following command.

aws iam attach-role-policy --role-name lambda-partition-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/lambda-partition-exec-policy

Following is the lambda-partition-function-execution-policy.json file, which is the IAM policy used by the Lambda execution role.

{
    "Version": "2012-10-17",
    "Statement": [
      	{
            	"Sid": "DDBTableAccess",
            	"Effect": "Allow",
            	"Action": "dynamodb:PutItem"
            	"Resource": "arn:aws:dynamodb*:*:table/athenapartitiondetails"
        	},
        	{
            	"Sid": "S3Access",
            	"Effect": "Allow",
            	"Action": [
                		"s3:GetBucketLocation",
                		"s3:GetObject",
                		"s3:ListBucket",
                		"s3:ListBucketMultipartUploads",
                		"s3:ListMultipartUploadParts",
                		"s3:AbortMultipartUpload",
                		"s3:PutObject"
            	],
          		"Resource":"arn:aws:s3:::*"
		},
	              {
		      "Sid": "AthenaAccess",
      		"Effect": "Allow",
      		"Action": [ "athena:*" ],
      		"Resource": [ "*" ]
	      },
        	{
            	"Sid": "CloudWatchLogsAccess",
            	"Effect": "Allow",
            	"Action": [
                		"logs:CreateLogGroup",
                		"logs:CreateLogStream",
             	   	"logs:PutLogEvents"
            	],
            	"Resource": "arn:aws:logs:*:*:*"
        	}
    ]
}

Download the .jar file containing the Java deployment package to your local machine.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/partitioning-lambda/aws-lambda-athena-1.0.0.jar <path_on_your_local_machine>

From the AWS Management Console, create a new Lambda function with Java8 as the runtime. Select the Blank Function blueprint.

On the Configure triggers page, choose the name of the S3 bucket where the preprocessed logs are delivered. Choose Put for the Event Type. For Prefix, type the name of the prefix folder, if any, where preprocessed logs are delivered by Firehose—for example, out/. For Suffix, type the name of the compression format that the Firehose stream (CloudFrontLogToS3) delivers the preprocessed logs —for example, gz. To invoke Lambda to retrieve the logs from the S3 bucket as they are delivered, select Enable Trigger.

Choose Next and provide a function name to identify this Lambda partitioning function.

Choose Java8 for Runtime for the AWS Lambda function. Choose Upload a .ZIP or .JAR file for the Code entry type, and choose Upload to upload the downloaded aws-lambda-athena-1.0.0.jar file.

Next, create the following environment variables for the Lambda function:

  • TABLE_NAME – The name of the Athena table (for example, cf_logs).
  • PARTITION_TYPE – The partition to be created based on the Athena table for the logs delivered to the sub folders in S3 bucket based on Year, Month, Date, Hour, or Set this to ALL to use Year, Month, Date, and Hour.
  • DDB_TABLE_NAME – The name of the DynamoDB table holding partition information (for example, athenapartitiondetails).
  • ATHENA_REGION – The current AWS Region for the Athena table to construct the JDBC connection string.
  • S3_STAGING_DIR – The Amazon S3 location where your query output is written. The JDBC driver asks Athena to read the results and provide rows of data back to the user (for example, s3://<bucketname>/<folder>/).

To configure the function handler and IAM, for Handler copy and paste the name of the handler: com.amazonaws.services.lambda.CreateAthenaPartitionsBasedOnS3EventWithDDB::handleRequest. Choose the existing IAM role, lambda-partition-execution-role.

Choose Next and then Create Lambda.

Interactive analysis using Amazon Athena

In this section, we analyze the historical data that’s been collected since we added the partitions to the Amazon Athena table for data delivered to the preprocessing logs bucket.

Scenario 1 is robot traffic by edge location.

SELECT COUNT(*) AS ct, requestip, location FROM cf_logs
WHERE isbot='True'
GROUP BY requestip, location
ORDER BY ct DESC;

Scenario 2 is total bytes transferred per distribution for each edge location for your website.

SELECT distribution, location, SUM(bytes) as totalBytes
FROM cf_logs
GROUP BY location, distribution;

Scenario 3 is the top 50 viewers of your website.

SELECT requestip, COUNT(*) AS ct  FROM cf_logs
GROUP BY requestip
ORDER BY ct DESC;

Streaming analysis using Amazon Kinesis Analytics

In this section, you deploy a stream processing application using Amazon Kinesis Analytics to analyze the preprocessed Amazon CloudFront log streams. This application analyzes directly from the Amazon Kinesis Stream as it is delivered to the preprocessing logs bucket. The stream queries in section are focused on gaining the following insights:

  • The IP address of the bot, identified by its Amazon CloudFront edge location, that is currently sending requests to your website. The query also includes the total bytes transferred as part of the response.
  • The total bytes served per distribution per population for your website.
  • The top 10 viewers of your website.

To download the firehose-access-policy.json file, type the following.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/kinesisanalytics/firehose-access-policy.json  <path_on_your_local_machine>

To download the kinesisanalytics-policy.json file, type the following.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis/kinesisanalytics/assume-kinesisanalytics-policy.json <path_on_your_local_machine>

Before we create the Amazon Kinesis Analytics application, we need to create the IAM role to provide permission for the analytics application to access Amazon Kinesis Firehose stream.

Let’s use the AWS CLI to create the IAM role using the following three steps:

  1. Create the IAM policy(firehose-access-policy) for the Lambda execution role to use.
  2. Create the Lambda execution role (ka-execution-role) and assign the service to use this role.
  3. Attach the policy created in step 1 to the Lambda execution role.

Following is the firehose-access-policy.json file, which is the IAM policy used by Kinesis Analytics to read Firehose delivery stream.

{
    "Version": "2012-10-17",
    "Statement": [
      	{
    	"Sid": "AmazonFirehoseAccess",
    	"Effect": "Allow",
    	"Action": [
       	"firehose:DescribeDeliveryStream",
        	"firehose:Get*"
    	],
    	"Resource": [
              "arn:aws:firehose:*:*:deliverystream/CloudFrontLogsToS3”
       ]
     }
}

Following is the assume-kinesisanalytics-policy.json file, to grant Amazon Kinesis Analytics permissions to assume a role.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "kinesisanalytics.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

To create the IAM policy used by Analytics access role, type the following command.

aws iam create-policy --policy-name firehose-access-policy --policy-document file://<path>/firehose-access-policy.json

To create the Analytics execution role and assign the service to use this role, type the following command.

aws iam attach-role-policy --role-name ka-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/firehose-access-policy

To attach the policy (irehose-access-policy) created to the Analytics execution role (ka-execution-role), type the following command.

aws iam attach-role-policy --role-name ka-execution-role --policy-arn arn:aws:iam::<your-account-id>:policy/firehose-access-policy

To deploy the Analytics application, first download the configuration file and then modify ResourceARN and RoleARN for the Amazon Kinesis Firehose input configuration.

"KinesisFirehoseInput": { 
    "ResourceARN": "arn:aws:firehose:<region>:<account-id>:deliverystream/CloudFrontLogsToS3", 
    "RoleARN": "arn:aws:iam:<account-id>:role/ka-execution-role"
}

To download the Analytics application configuration file, type the following command.

aws s3 cp s3://aws-bigdata-blog/artifacts/Serverless-CF-Analysis//kinesisanalytics/kinesis-analytics-app-configuration.json <path_on_your_local_machine>

To deploy the application, type the following command.

aws kinesisanalytics create-application --application-name "cf-log-analysis" --cli-input-json file://<path>/kinesis-analytics-app-configuration.json

To start the application, type the following command.

aws kinesisanalytics start-application --application-name "cf-log-analysis" --input-configuration Id="1.1",InputStartingPositionConfiguration={InputStartingPosition="NOW"}

SQL queries using Amazon Kinesis Analytics

Scenario 1 is a query for detecting bots for sending request to your website detection for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "BOT_DETECTION" (requesttime TIME, destribution VARCHAR(16), requestip VARCHAR(64), edgelocation VARCHAR(64), totalBytes BIGINT);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "BOT_DETECTION_PUMP" AS INSERT INTO "BOT_DETECTION"
--
SELECT STREAM 
    STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND) as requesttime,
    "distribution_name" as distribution,
    "request_ip" as requestip, 
    "edge_location" as edgelocation, 
    SUM("bytes") as totalBytes
FROM "CF_LOG_STREAM_001"
WHERE "is_bot" = true
GROUP BY "request_ip", "edge_location", "distribution_name",
STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND),
STEP("CF_LOG_STREAM_001".ROWTIME BY INTERVAL '1' SECOND);

Scenario 2 is a query for total bytes transferred per distribution for each edge location for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "BYTES_TRANSFFERED" (requesttime TIME, destribution VARCHAR(16), edgelocation VARCHAR(64), totalBytes BIGINT);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "BYTES_TRANSFFERED_PUMP" AS INSERT INTO "BYTES_TRANSFFERED"
-- Bytes Transffered per second per web destribution by edge location
SELECT STREAM 
    STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND) as requesttime,
    "distribution_name" as distribution,
    "edge_location" as edgelocation, 
    SUM("bytes") as totalBytes
FROM "CF_LOG_STREAM_001"
GROUP BY "distribution_name", "edge_location", "request_date",
STEP("CF_LOG_STREAM_001"."request_time" BY INTERVAL '1' SECOND),
STEP("CF_LOG_STREAM_001".ROWTIME BY INTERVAL '1' SECOND);

Scenario 3 is a query for the top 50 viewers for your website.

-- Create output stream, which can be used to send to a destination
CREATE OR REPLACE STREAM "TOP_TALKERS" (requestip VARCHAR(64), requestcount DOUBLE);
-- Create pump to insert into output 
CREATE OR REPLACE PUMP "TOP_TALKERS_PUMP" AS INSERT INTO "TOP_TALKERS"
-- Top Ten Talker
SELECT STREAM ITEM as requestip, ITEM_COUNT as requestcount FROM TABLE(TOP_K_ITEMS_TUMBLING(
  CURSOR(SELECT STREAM * FROM "CF_LOG_STREAM_001"),
  'request_ip', -- name of column in single quotes
  50, -- number of top items
  60 -- tumbling window size in seconds
  )
);

Conclusion

Following the steps in this blog post, you just built an end-to-end serverless architecture to analyze Amazon CloudFront access logs. You analyzed these both in interactive and streaming mode, using Amazon Athena and Amazon Kinesis Analytics respectively.

By creating a partition in Athena for the logs delivered to a centralized bucket, this architecture is optimized for performance and cost when analyzing large volumes of logs for popular websites that receive millions of requests. Here, we have focused on just three common use cases for analysis, sharing the analytic queries as part of the post. However, you can extend this architecture to gain deeper insights and generate usage reports to reduce latency and increase availability. This way, you can provide a better experience on your websites fronted with Amazon CloudFront.

In this blog post, we focused on building serverless architecture to analyze Amazon CloudFront access logs. Our plan is to extend the solution to provide rich visualization as part of our next blog post.


About the Authors

Rajeev Srinivasan is a Senior Solution Architect for AWS. He works very close with our customers to provide big data and NoSQL solution leveraging the AWS platform and enjoys coding . In his spare time he enjoys riding his motorcycle and reading books.

 

Sai Sriparasa is a consultant with AWS Professional Services. He works with our customers to provide strategic and tactical big data solutions with an emphasis on automation, operations & security on AWS. In his spare time, he follows sports and current affairs.

 

 


Related

Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight

Copyright Troll Piracy ‘Witness’ Went Back to the Future – and Lost

Post Syndicated from Andy original https://torrentfreak.com/copyright-troll-piracy-witness-went-back-to-the-future-and-lost-170526/

Since the early 2000s, copyright trolls have been attempting to squeeze cash from pirating Internet users and fifteen years later the practice is still going strong.

While there’s little doubt that trolls catch some genuine infringers in their nets, the claim that actions are all about protecting copyrights is a shallow one. The aim is to turn piracy into profit and history has shown us that the bigger the operation, the more likely it is they’ll cut corners to cut costs.

The notorious Guardaley trolling operation is a prime example. After snaring the IP addresses of hundreds of thousands of Internet users, the company extracts cash settlements in the United States, Europe and beyond. It’s a project of industrial scale based on intimidation of alleged infringers. But, when those people fight back, the scary trolls suddenly become less so.

The latest case of Guardaley running for the hills comes courtesy of SJD from troll-watching site FightCopyrightTrolls, who reports on an attempt by Guardaley partner Criminal Productions to extract settlement from Zach Bethke, an alleged downloader of the Ryan Reynolds movie, Criminal.

On May 12, Bethke’s lawyer, J. Christopher Lynch, informed Criminal Productions’ lawyer David A. Lowe that Bethke is entirely innocent.

“Neither Mr. Bethke nor his girlfriend copied your client’s movie and they do not know who, if anyone, may have done so,” Lynch wrote.

“Mr. Bethke does not use BitTorrent. Prior to this lawsuit, Mr. Bethke had never heard of your client’s movie and he has no interest in it. If he did have any interest in it, he could have rented it for no marginal cost using his Netflix or Amazon Prime accounts.”

Lynch went on to request that Criminal Productions drop the case. Failing that, he said, things would probably get more complicated. As reported last year, Lynch and Lowe have been regularly locking horns over these cases, with Lynch largely coming out on top.

Part of Lynch’s strategy has been to shine light on Guardaley’s often shadowy operations. He previously noted that its investigators were not properly licensed to operate in the U.S. and the company had been found to put forward a fictitious witness, among other things.

In the past, these efforts to bring Guardaley out into the open have resulted in its clients’, which include several film companies, dropping cases. Lynch, it appears, wants that to happen again in Bethke’s case, noting in his letter that it’s “long past due for a judge to question the qualifications” of the company’s so-called technical experts.

In doing so he calls Guardaley’s evidence into account once more, noting inconsistencies in the way alleged infringements were supposedly “observed” by “foreign investigator[s], with a direct financial interest in the matter.”

One of Lynch’s findings is that the “observations” of two piracy investigators overlap each others’ monitoring periods in separate cases, while reportedly monitoring the same torrent hash.

“Both declarations cover the same ‘hash number’ of the movie, i.e. the same soak. This overlap seems impossible if we stick with the fictions of the Complaint and Motion for Expedited Discovery that the declarant ‘observed’ the defendant ‘infringing’,” Lynch notes.

While these are interesting points, the quality of evidence presented by Guardaley and Criminal Productions is really called into question following another revelation. Daniel Macek, an ‘observing’ investigator used in numerous Guardaley cases, apparently has a unique talent.

As seen from the image below, the alleged infringements relating to Mr. Bethke’s case were carried out between June 25 and 28, 2016.

However, the declaration (pdf) filed with the Court on witness Macek’s behalf was signed and dated either June 14 or 16, more than a week before the infringements allegedly took place.

Time-traveler? Lynch thinks not.

“How can a witness sign a declaration that he observed something BEFORE it happened?” he writes.

“Criminal Productions submitted four such Declarations of Mr. Macek that were executed BEFORE the dates of the accompanying typed up list of observations that Mr. Macek swore that he made.

“Unless Daniel Macek is also Marty McFly, it is impossible to execute a declaration claiming to observe something that has yet to happen.”

So what could explain this strange phenomenon? Lynch believes he’s got to the bottom of that one too.

After comparing all four Macek declarations, he found that aside from the case numbers, the dates and signatures were identical. Instead of taking the issue of presenting evidence before the Court seriously, he believes Criminal Productions and partner Guardaley have been taking short cuts.

“From our review, it appears these metaphysical Macek declarations are not just temporally improper, they are also photocopies, including the signatures not separately executed,” he notes.

“We are astonished by your client’s foreign representatives’ apparent lack of respect for our federal judicial system. Use of duplicate signatures from a witness testifying to events that have yet to happen is on the same level of horror as the use of a fictitious witness and ‘his’ initials as a convenience to obtain subpoenas.”

Not entirely unexpectedly, five days later the case against Bethke and other defendants was voluntarily dismissed (pdf), indicating once again that like vampires, trolls do not like the light. Other lawyers defending similar cases globally should take note.

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

[$] The state of bugs.python.org

Post Syndicated from jake original https://lwn.net/Articles/723513/rss

In a brief session at the 2017 Python Language Summit, Maciej Szulik gave
an update on the state and plans for bugs.python.org (bpo). It is the Roundup-based bug tracker for
Python; moving to GitHub has not changed that. He described the work that
two Google Summer of Code (GSoC) students have done to improve the bug
tracker.

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/723048/rss

Security updates have been issued by Arch Linux (libplist), Debian (mysql-connector-java), Fedora (jasper, kdelibs, lxterminal, menu-cache, pcmanfm, and postgresql), openSUSE (qemu), Slackware (freetype and kdelibs), SUSE (ghostscript-library, libtirpc, and mariadb), and Ubuntu (ghostscript, kernel, linux, linux-raspi2, linux-hwe, openjdk-7, qemu, shadow, and thunderbird).

EC2 In-Memory Processing Update: Instances with 4 to 16 TB of Memory + Scale-Out SAP HANA to 34 TB

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/ec2-in-memory-processing-update-instances-with-4-to-16-tb-of-memory-scale-out-sap-hana-to-34-tb/

Several times each month, I speak to AWS customers at our Executive Briefing Center in Seattle. I describe our innovation process and talk about how the roadmap for each AWS offering is driven by customer requests and feedback.

A good example of this is our work to make AWS a great home for SAP’s portfolio of business solutions. Over the years our customers have told us that they run large-scale SAP applications in production on AWS and we’ve worked hard to provide them with EC2 instances that are designed to accommodate their workloads. Because SAP installations are unfailingly mission-critical, SAP certifies their products for use on certain EC2 instance types and sizes. We work directly with SAP in order to achieve certification and to make AWS a robust & reliable host for their products.

Here’s a quick recap of some of our most important announcements in this area:

June 2012 – We expanded the range of SAP-certified solutions that are available on AWS.

October 2012 – We announced that the SAP HANA in-memory database is now available for production use on AWS.

March 2014 – We announced that SAP HANA can now run in production form on cr1.8xlarge instances with up to 244 GB of memory, with the ability to create test clusters that are even larger.

June 2014 – We published a SAP HANA Deployment Guide and a set of AWS CloudFormation templates in conjunction with SAP certification on r3.8xlarge instances.

October 2015 – We announced the x1.32xlarge instances with 2 TB of memory, designed to run SAP HANA, Microsoft SQL Server, Apache Spark, and Presto.

August 2016 – We announced that clusters of X1 instances can now be used to create production SAP HANA clusters with up to 7 nodes, or 14 TB of memory.

October 2016 – We announced the x1.16xlarge instance with 1 TB of memory.

January 2017 – SAP HANA was certified for use on r4.16xlarge instances.

Today, customers from a broad collection of industries run their SAP applications in production form on AWS (the SAP and Amazon Web Services page has a long list of customer success stories).

My colleague Bas Kamphuis recently wrote about Navigating the Digital Journey with SAP and the Cloud (registration required). He discusses the role of SAP in digital transformation and examines the key characteristics of the cloud infrastructure that support it, while pointing out many of the advantages that the cloud offers in comparison to other hosting options. Here’s how he illustrates these advantages in his article:

We continue to work to make AWS an even better place to run SAP applications in production form. Here are some of the things that we are working on:

  • Bigger SAP HANA Clusters – You can now build scale-out SAP HANA clusters with up to 17 nodes (34 TB of memory).
  • 4 TB Instances – The upcoming x1e.32xlarge instances will offer 4 TB of memory.
  • 8 – 16 TB Instances – Instances with up to 16 TB of memory are in the works.

Let’s dive in!

Building Bigger SAP HANA Clusters
I’m happy to announce that we have been working with SAP to certify the x1.32large instances for use in scale-out clusters with up to 17 nodes (34 TB of memory). This is the largest scale-out deployment available from any cloud provider today, and allows our customers to deploy very large SAP workloads on AWS (visit the SAP HANA Hardware directory certification for the x1.32xlarge instance to learn more). To learn how to architect and deploy your own scale-out cluster, consult the SAP HANA on AWS Quick Start.

Extending the Memory-Intensive X1 Family
We will continue to invest in this and other instance families in order to address your needs and to give you a solid growth path.

Later this year we plan to make the x1e.32xlarge instances available in several AWS regions, in both On-Demand and Reserved Instance form. These instances will offer 4 TB of DDR4 memory (twice as much as the x1.32xlarge), 128 vCPUs (four 2.3 GHz Intel® Xeon® E7 8880 v3 processors), high memory bandwidth, and large L3 caches. The instances will be VPC-only, and will deliver up to 20 Gbps of network banwidth using the Elastic Network Adapter while minimizing latency and jitter. They’ll be EBS-optimized by default, with up to 14 Gbps of dedicated EBS throughput.

Here are some screen shots from the shell. First, dmesg shows the boot-time kernel message:

Second, lscpu shows the vCPU & socket count, along with many other interesting facts:

And top shows nearly 900 processes:

Here’s the view from within HANA Studio:

This new instance, along with the certification for larger clusters, broadens the set of scale-out and scale-up options that you have for running SAP on EC2, as you can see from this diagram:

The Long-Term Memory-Intensive Roadmap
Because we know that planning large-scale SAP installations can take a considerable amount of time, I would also like to share part of our roadmap with you.

Today, customers are able to run larger SAP HANA certified servers in third party colo data centers and connect them to their AWS infrastructure via AWS Direct Connect, but customers have told us that they really want a cloud native solution like they currently get with X1 instances.

In order to meet this need, we are working on instances with even more memory! Throughout 2017 and 2018, we plan to launch EC2 instances with between 8 TB and 16 TB of memory. These upcoming instances, along with the x1e.32xlarge, will allow you to create larger single-node SAP installations and multi-node SAP HANA clusters, and to run other memory-intensive applications and services. It will also provide you with some scale-up headroom that will become helpful when you start to reach the limits of the smaller instances.

I’ll share more information on our plans as soon as possible.

Say Hello at SAPPHIRE
The AWS team will be in booth 539 at SAPPHIRE with a rolling set of sessions from our team, our customers, and our partners in the in-booth theater. We’ll also be participating in many sessions throughout the event. Here’s a sampling (see SAP SAPPHIRE NOW 2017 for a full list):

SAP Solutions on AWS for Big Businesses and Big Workloads – Wednesday, May 17th at Noon. Bas Kamphuis (General Manager, SAP, AWS) & Ed Alford (VP of Business Application Services, BP).

Break Through the Speed Barrier When You Move to SAP HANA on AWS – Wednesday, May 17th at 12:30 PM – Paul Young (VP, SAP) and Saul Dave (Senior Director, Enterprise Systems, Zappos).

AWS Fireside Chat with Zappos (Rapid SAP HANA Migration: Real Results) – Thursday, May 18th at 11:00 AM – Saul Dave (Senior Director, Enterprise Systems, Zappos) and Steve Jones (Senior Manager, SAP Solutions Architecture, AWS).

Jeff;

PS – If you have some SAP experience and would like to bring it to the cloud, take a look at the Principal Product Manager (AWS Quick Starts) and SAP Architect positions.

Security updates for Monday

Post Syndicated from ris original https://lwn.net/Articles/722774/rss

Security updates have been issued by Arch Linux (git, lxc, openvpn, and zziplib), Debian (bind9, bitlbee, postgresql-9.4, rtmpdump, sane-backends, and squirrelmail), Fedora (ghostscript, git, kdelibs, kf5-kauth, libplist, libreoffice, openvpn, php-horde-ingo, qemu, radicale, rpcbind, and xen), and Ubuntu (git and kde4libs).

Judge Threatens to Bar ‘Copyright Troll’ Cases Over Lacking IP-location Evidence

Post Syndicated from Ernesto original https://torrentfreak.com/judge-threatens-to-bar-copyright-troll-cases-over-lacking-ip-location-evidence-170212/

While relatively underreported, many U.S. district courts are still swamped with lawsuits against alleged film pirates.

The copyright holders who initiate these cases generally rely on an IP address as evidence. This information is collected from BitTorrent swarms and linked to a geographical location using geolocation tools.

With this information in hand, they then ask the courts to grant a subpoena, directing Internet providers to hand over the personal details of the associated account holders.

Malibu Media, the Los Angeles-based company behind the ‘X-Art’ adult movies, is behind most of these cases. The company has filed thousands of lawsuits in recent years, targeting Internet subscribers whose accounts were allegedly used to share Malibu’s films via BitTorrent.

Increasingly, judges around the country have grown wary of these litigation efforts. This includes US Federal Judge William Alsup, who’s tasked with handling all such cases in the Northern District of California.

Responding to a recent request, Judge Alsup highlights the fact that Malibu filed a “monsoon” of hundreds of lawsuits over the past 18 months, but later dismissed many of them after without specifying a reason.

The judge is skeptical about the motivation for these dismissals. In particular, because courts have previously highlighted that Maxmind’s geolocation tools, which are cited in the complaints, may not be entirely accurate. This could mean that the cases have been filed in the wrong court.

“Malibu Media’s voluntary dismissal without prejudice of groups of its cases is not a new pattern. A sizable portion of the cases from previous waves were terminated in the same way,” Judge Alsup writes (pdf).

“The practice has just become more frequent, and it follows skepticism by the undersigned judge and others around the country about the accuracy of the Maxmind database,” he adds.

This is not the first time that geolocation tools have been called into doubt and to move the accuracy claims beyond Maxmind’s own “hearsay,” Judge Alsup now demands extra evidence.

In his order he denies the request to continue a case management conference in one of their cases. Instead, he will use that hearing to address the geolocation issues. In addition, all Malibu cases in the district may be barred if the accuracy of these tools isn’t “fully vetted.”

“That request is DENIED. Instead, Malibu Media is hereby ordered to SHOW CAUSE at that hearing, why the Court should not bar further Malibu Media cases in this district until the accuracy of the geolocation technology is fully vetted,” the order reads.

“To be clear, this order applies even if Malibu Media voluntarily dismisses this action,” Judge Alsup adds.

Denied

SJD, who follows the developments closely and first reported on the order, suspects that the IP-address ‘error rate’ may in fact be higher than most people believe. She therefore recommends defense lawyer to depose ISP employees to get to the bottom of the issue.

“If you are a defense attorney who litigates one of the BitTorrent infringement cases, I suggest deposing a Comcast employee tasked with subpoena processing. I suspect that the error rate is much higher than trolls want everyone to believe, and such testimony has a potential to become a heavy weapon in every troll victim’s arsenal,” SJD says.

In any case, it’s no secret that geolocation databases are far from perfect. Most are not updated instantly, which means that the information could be outdated, and other entries are plainly inaccurate.

This is something the residents of a Kansas farm know all too well, as their house is the default location of 600 million IP-addresses, which causes them quite a bit of trouble.

It will be interesting to see if Malibu will make any efforts to properly “vet” Maxmind’s database. It’s clear, however, that Judge Alsup will not let the company use his court before fully backing up their claims.

To be continued.

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

Security updates for Friday

Post Syndicated from jake original https://lwn.net/Articles/722589/rss

Security updates have been issued by Debian (kde4libs), Fedora (elfutils, libplist, mediawiki, and xen), Red Hat (chromium-browser and ghostscript), Scientific Linux (ghostscript), SUSE (kernel and MozillaFirefox, mozilla-nss, mozilla-nspr, java-1_8_0-openjdk), and Ubuntu (firefox, lightdm, openjdk-8, and openvpn).

Hold ISPs Responsible For Piracy After Brexit, Music Biz Says

Post Syndicated from Andy original https://torrentfreak.com/hold-isps-responsible-for-piracy-after-brexit-music-biz-says-170512/

UK Music is an umbrella organization representing music interests in the UK, from artists and composers, through to studios, recording labels and collecting societies.

The group counts many influential bodies as members, including the BPI, PRS for Music, and licensing outfit PPL. No surprise then that it has a keen anti-piracy agenda, much in tune with its member groups.

Yesterday, UK Music published its 2017 manifesto, covering a wide range of topics from regional development, skills and education, to finance and investment. Needless to say, anti-piracy measures feature prominently, with the group urging vigilance during the Brexit process to ensure music gets a good deal.

“Copyright and its enforcement should be a key part of the trade negotiations, ensuring that our trading partners protect not only their respective creative industries but also the interests of the UK music industry,” the group says.

“Maintaining and strengthening the copyright framework is of great importance to the music industry during the Brexit negotiations and beyond.”

When the UK leaves the EU mid-2019, the government proposes to convert all EU law into UK law. According to David Davis, the Secretary of State for Exiting the European Union, the so-called Great Repeal Bill will provide “clarity and certainty” for businesses and citizens alike.

However, the Bill will also grant power for MPs to change these laws once the UK has left the EU. For UK Music, this should be a time for stability for the music business.

“Withdrawal from the EU does not require substantial changes to the UK copyright framework. This continuity is critical to ensuring confidence amongst music businesses,” the group says.

“There is no evidence of the need for new exceptions to copyright. If this is not accepted by the Government then it would only serve to take away rights and undermine the potential for growth.”

But while stressing the importance of post-Brexit stability for the music industry, UK Music sees no problem with changing the law to impose additional responsibilities on others.

“There were 7.2 billion visits to copyright-infringing stream-ripping websites in 2016, representing a 60% increase in the previous year. Withdrawal from the EU provides an opportunity for the UK to strengthen the enforcement of copyright,” the group says.

That toughening-up of the law should be focused on tech companies, UK Music insists.

“Initiatives should be developed to place responsibility on internet service providers and require them to have a duty of care for copyright protected music,” the group says.

While UK Music has a clear mandate to look after its own interests, it’s likely that service providers would also like the opportunity to enjoy both continuity and stability after the Brexit negotiations are complete. Being held responsible for piracy is unlikely to help them reach that goal.

Nevertheless, UK Musicwill require further support from ISPs, if it is to meet another of its manifesto goals. Currently, several of the UK’s largest providers are cooperating with the industry to send piracy notices to their subscribers. UK Music would like to expand the scheme.

“The Get It Right From A Genuine Site campaign, designed to promote greater copyright understanding online, is also showing evidence of success. With further support it has the potential to broaden its reach,” the organization says.

Finally, UK Music says that Brexit will give the UK an opportunity to put forward “a coherent definition of hyperlinking under copyright law.”

The group doesn’t go into specifics, but it could be argued that the recent GS Media case handled by the European Court of Justice offers all the clarity the UK needs to transfer the decision into local law.

The full manifesto can be downloaded here (pdf)

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

John Oliver is wrong about Net Neutrality

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/05/john-oliver-is-wrong-about-net.html

People keep linking to John Oliver bits. We should stop doing this. This is comedy, but people are confused into thinking Oliver is engaging in rational political debate:
Enlightened people know that reasonable people disagree, that there’s two sides to any debate. John Oliver’s bit erodes that belief, making one side (your side) sound smart, and the other side sound unreasonable.
The #1 thing you should know about Net Neutrality is that reasonable people disagree. It doesn’t mean they are right, only that they are reasonable. They aren’t stupid. They aren’t shills for the telcom lobby, or confused by the telcom lobby. Indeed, those opposed to Net Neutrality are the tech experts who know how packets are routed, whereas the supporters tend only to be lawyers, academics, and activists. If you think that the anti-NetNeutrality crowd is unreasonable, then you are in a dangerous filter bubble.
Most everything in John Oliver’s piece is incorrect.
For example, he says that without Net Neutrality, Comcast can prefer original shows it produces, and slow down competing original shows by Netflix. This is silly: Comcast already does that, even with NetNeutrality rules.
Comcast owns NBC, which produces a lot of original shows. During prime time (8pm to 11pm), Comcast delivers those shows at 6-mbps to its customers, while Netflix is throttled to around 3-mbps. Because of this, Comcast original shows are seen at higher quality than Netflix shows.
Comcast can do this, even with NetNeutrality rules, because it separates its cables into “channels”. One channel carries public Internet traffic, like Netflix. The other channels carry private Internet traffic, for broadcast TV shows and pay-per-view.
All NetNeutrality means is that if Comcast wants to give preference to its own contents/services, it has to do so using separate channels on the wire, rather than pushing everything over the same channel. This is a detail nobody tells you because NetNeutrality proponents aren’t techies. They are lawyers and academics. They maximize moral outrage, while ignoring technical details.
Another example in Oliver’s show is whether search engines like Google or the (hypothetical) Bing can pay to get faster access to customers. They already do that. The average distance a packet travels on the web is less than 100-miles. That’s because the biggest companies (Google, Facebook, Netflix, etc.) pay to put servers in your city close to you. Smaller companies, such as search engine DuckDuckGo.com, also pay third-party companies like Akamai or Amazon Web Services to get closer to you. The smallest companies, however, get poor performance, being a thousand miles away.
You can test this out for yourself. Run a packet-sniffer on your home network for a week, then for each address, use mapping tools like ping and traceroute to figure out how far away things are.
The Oliver bit mentioned how Verizon banned Google Wallet. Again, technical details are important here. It had nothing to do with Net Neutrality issues blocking network packets, but only had to do with Verizon-branded phones blocking access to the encrypted enclave. You could use Google Wallet on unlocked phones you bought separately. Moreover, market forces won in the end, with Google Wallet (aka. Android Wallet) now the preferred wallet on their network. In other words, this incident shows that the “free market” fixes things in the long run without the heavy hand of government.
Oliver shows a piece where FCC chief Ajit Pai points out that Internet companies didn’t do evil without Net Neutrality rules, and thus NetNeutrality rules were unneeded. Oliver claimed this was a “disingenuous” argument. No, it’s not “disingenuous”, it entirely the point of why Net Neutrality is bad. It’s chasing theoretical possibility of abuse, not the real thing. Sure, Internet companies will occasionally go down misguided paths. If it’s truly bad, customers will rebel. In some cases, it’s not actually a bad thing, and will end up being a benefit to customers (e.g. throttling BitTorrent during primetime would benefit most BitTorrent users). It’s the pro-NetNeutrality side that’s being disingenuous, knowingly trumping up things as problems that really aren’t.
The point is this. The argument here is a complicated one, between reasonable sides. For humor, John Oliver has created a one-sided debate that falls apart under any serious analysis. Those like the EFF should not mistake such humor for intelligent technical debate.

Security updates for Tuesday

Post Syndicated from ris original https://lwn.net/Articles/722246/rss

Security updates have been issued by Debian (libtirpc and libytnef), Fedora (python-fedora, roundcubemail, and tnef), Mageia (ntp and virtualbox), openSUSE (dpkg, ghostscript, kernel, libressl, mysql-community-server, quagga, tcpdump, libpcap, xen, and zziplib), Red Hat (java-1.7.0-openjdk), Scientific Linux (java-1.7.0-openjdk), and SUSE (samba).