Deflect Labs

Deflect Labs report #3

Botnet attack analysis of Deflect protected website blacklivesmatter.com

Seamus Tuohy and eQualit.ie

View the report with 3D rendering (5mb)

This report covers attacks between April 29th and October 15th, 2016. Over this seven-month period, we recorded more than a hundred separate denial-of-service incidents against the official Black Lives Matter website. Our analysis shows a variety of technical methods used in attempts to bring down this website and the characterization of these attacks point to a “mob” mentality of malicious actors jumping on board in response to callouts made on social media and covert channels. Our reporting highlights the usage of no-questions-asked-hosting and booter services used by malicious actors to carry out these attacks. We describe the ever growing trend of Internet vandals who, searching for a little bit of infamy, launch denial-of-service attacks against the Black Lives Matter (BLM) website. Our analysis documented attacks that could be accomplished for as little as $1 and, with access to public documentation and malicious software within easy reach, only required basic technical skill. Some of the larger attacks against BLM generated millions of connections without relying on huge infrastructure. Instead, traffic was “reflected” from legitimate WordPress and Joomla sites. We compare public attribution for some of the attacks with the data coming through our networks, and present the involvement of purported members of the Ghost Squad Hackers crew in these events.

Contents:

Introduction

“Black Lives Matter, a May First/People Link member that is supported by the Design Action Collective, is a central organization in the response movement against police abuse, brutality and misconduct.” The BLM website has been protected by Deflect since April 15th, 2016, following a spate of DDoS and hacking attacks.

In early July we published a prima facie bulletin expecting to write a comprehensive report of the attacks soon after. Since then the BLM website faced an increasing number of sizable attacks that we decided to include in our analysis and delayed publication. This report will explore these attacks, correlating open source research and publicly stated attribution with what we saw in the data.

The Deflect Labs infrastructure allows us to capture, process and profile each attack, analyzing unique incidents and intersecting findings with a database of profiled botnets. We define the parameters for anomalous behavior on the network and then group (“cluster”) malicious IPs into botnets using unsupervised machine learning algorithms.

 

Attacks & attribution

As a DDoS mitigation solution for blacklivesmatter.com, Deflect has access to all legitimate and malicious requests made to this website. However in almost all cases, attacks come via infected machines or as reflection attacks from unsuspecting websites. A semi-experienced attacker knows how to obfuscate and disguise their traces on the Internet. It is therefore incredibly difficult to attribute an action to a particular person or IP address with confidence. We rely on our analytic tooling, peers in the mitigation industry and social media research to test our hypotheses. Assumptions arising out of OSINT are then verified against the data on our systems and vice versa.

Technical analysis and social media research indicated that actions against the BLM website were launched by multiple attackers frequently acting in concert. Some methods, like Joomla & WordPress reflection attacks, appear to have been coordinated, whilst in other cases it was clear that many actors jumped on the bandwagon of a more powerful attack to claim some of the credit. These small, loosely organized mobs appear minutes to hours after the start of the original attack and lob a hodge-podge of various attack methods, often to no effect. These actions are often accompanied by a flurry of queries from various website downtime monitoring solutions, as attackers try to collect trophies for their participation in the mob. Furthermore, we noticed a sophisticated actor who was able to generate malicious traffic on a level beyond anyone else that we documented targeting BLM. Using bulletproof hosting to coordinate their attacks, they did not go to great lengths to obfuscate their identity, creating instead a complicated web of social media accounts, possibly fake public attribution claims, and general intrigue about their motivations and purpose.

The ‘Ghost Squad’

The first, and only, publicly attributed attacks began in late April, as _s1ege, a professed member of the Ghost Squad Hackers crew, began tweeting screenshots showing site defacement and reports from website up-time checkers that the BLM site was no longer reachable. The action was part of #OPAllLivesMatter, likely in response to the #AllLivesMatter slogan (and then hashtag) created in 2015. On May 2nd, 2016, a YouTube video uploaded by @anonymous_exposes_racism contained a warning from a group identifying themselves as Anonymous to leaders of the Black Lives Matter movement, asking them to also denounce anti-white racism.

This first set of attacks against BLM, beginning on April 29th, lasted a mere 30 minutes. They came from six IP addresses and generated a little under 15,000 connections. A single method of attack and very few resources were brought into play, making this small action only temporarily effective at best. That evening five different IP addresses conducted another attack against the BLM website that topped off at over 158,000 connections over a period of an hour.

Timelion expression tracking malicious connections, by attack method, on April 29th

During this attack @_s1ege posted Twitter comments taking credit. Alongside photos that showed the Black Lives Matter website had been temporarily taken down by this attack, _s1ege posted a photo of the software he was using, “BlackHorizon”.

Timelion expression tracking malicious connections, by attack method, on April 29th

During this attack @_s1ege posted Twitter comments taking credit. Alongside photos that showed the Black Lives Matter website had been temporarily taken down by this attack, _s1ege posted a photo of the software he was using, “BlackHorizon”.

BlackHorizon is a clone of a piece of HTTP DoS software called GoldenEye, which was written by Jan Seidl in 2014. It was itself an expansion on the 2012 HULK project by Barry Shteiman. Unlike Seidl’s thoughtful adaptation and expansion of HULK, the BlackHorizon codebase mainly changes the ASCII art and the author’s name. When examined, it was clear that the functional components of the code were almost entirely unaltered from GoldenEye.

Several media publications rushed to interview _s1ege, with the @ghostsquadhack Twitter and GhostSquadHackers Facebook account referencing these publications. Around 30 minutes after the second attack Waqas Amir published an article on HackRead describing both incidents alongside his conversation with a GSH member. Later that evening one member of the GSH came back reusing an earlier bot and creating an attack that generated well under 700 connections, before giving up after less than 20 minutes.

Shortly after the tweets and HackRead publication, we witnessed an increase in attack frequency and variety. Only a portion of these had a similar behavioral profile on the network to those attributed by _s1ege to GSH. The attackers were using well-known software and may have called out to others on the Internet to follow suit. On May 10th, @_s1ege announces @bannedoffline as a new member in the Ghost Squad crew and two days later stops tweeting from this account altogether.

Maskirovka

BLM began to face larger scale attacks on May 9th. The first one lasted a little over 90 minutes and consisted of 1,022,981 connections from legitimate WordPress websites. This was not the first WordPress pingback attack against the BLM website, but it was an indication that we were beginning to face adversaries prepared to deploy much greater resources than before.The level of severity and aggression continued to mount and on July 9th we witnessed a WordPress pingback attack that generated over 34 million connections to BLM in a single day. The attackers did not seem to be interested in obfuscating their provenance, allowing us to track these activities over the next few months. The attacks were coordinated from machines hosted at a “bulletproof” provider – so called because they offer servers for rent on a no-questions-asked basis. The incidents associated with these attacks were the largest faced by BLM during the reporting period.

On July 25th we received a subscription for Deflect protection from a “John Smith” asking us to enlist http://ghostsquadhackers.org. We traced this request and further conversation with this user to @bannedoffline on Twitter and Facebook, as well as the owner of the following domains: ghostantiddos.com; ghostsquadsecurity.com; bannedoffline.xyz; www.btcsetmefree.org, among others.

Our analysis of actions run from the “bulletproof” hosting provider identified several IP addresses that were used for command and control. These addresses were correlated by a peer mitigation provider who had dared @bannedoffline on a hackers forum to DDoS them and recorded the resulting activity. Two IP addresses, one belonging to the DMZhosting provider mentioned further on in this report and a Digital Ocean machine, were identified in our individual records – and correlated to eight separate incidents in our study.

  • 191.96.249.80 Dmzhost Limited https://dmzhost.co
  • 178.62.152.134 DigitalOcean https://www.digitalocean.com

It is hard to say with any certainty why there were no more public attributions for attacks on BLM after the first week of May, considering that the severity and sophistication increased several-fold. @bannedoffline deleted all of their social media postings in late September, just before we recorded the biggest attack against the BLM website. bannedoffline was also linked to a 665gbps attack (the largest attack of its time, before the Mirai botnets) against the Krebs on Security website. The Ghost Squad did not attribute or deny @bannedoffline’s continued participation in their crew. Attacks attributable to bannedoffline and _s1ege, who could very well be the same person, made up less than 20% of recorded DDoS activity against BLM.

Technical Analysis Of Attacks

Incidents using a similar attack method were distinguished through an iterative process of identifying possible behavioral characteristics that distinguish one type of attack from others. First we identified combinations of behaviors and features that distinguished possible attacks from normal traffic. These profiles were then matched to existing types of attacks by looking for signatures from other reports and known codebases of these attacks to create an attack method profile. At this point secondary characteristics of the attack were examined to see if they distinguished individual attacks. This ranged from the hosting provider used for botherders, to the collection of innocent websites used as reflectors, and the methods used to check the status of the website, among others. If one or more of these characteristics overlapped for a specific set of attacks, those attacks were flagged for further investigation. Once we clustered these attacks, we looked across the entire set of attacks and attempted to reject any characteristic that could clearly differentiate that subset of attacks from similar attacks.

The most common category of attacks against the BLM website has been “application level” (layer 7) HTTP flood attacks. These bots mimic human behavior by connecting to a website and requesting a large amount of content until the server crashes for lack of resources. In this report we will only be looking at this type of attacks.

The capability of individual attackers has ranged greatly. As the BLM website faced more resourced and effective attackers, the mob became a persistent background noise.

Attack type (including variants and clones) April May June July Aug Sept Oct
WordPress pingback 5 6 4 4 5
Joomla pingback 1 6 6 4 3 3
Slow Loris 2 5 3 1
Fully Randomized NoCache Flood 6 14 11 5 7 2 4
Cache Bypass flood 1 1 2 2
Python script flood 2 2

You can view the entire attack portfolio on Google Docs


Slowloris

Aliases/Tools Slowloris, Pyloris, Torloris
Attack Type Layer 7 Denial of Service
Exploits Connection exhaustion
Obfuscation None
Attack Class Single-source
Attack Rate Low

The first attack identified against the Black Lives Matters website occurred on April 18th, just a few days after it had switched over to Deflect. A single address made between 5 and 30 connections per second to the main BLM web page. This lasted for 28 seconds. In total it made only 168 connections. Usually, this type of behavior would not raise any flags. But in this case, the user agent of this client matched the user agent used in the original proof of concept code for “Slowloris – the low bandwidth, yet greedy and poisonous HTTP client!”

Slowloris is a DoS tool that was originally released in 2009. It is unique among the other Layer 7 attacks we will be discussing in this report because it does not focus on flooding the network with traffic. Instead, it attempts to use up all the connections to a web server leaving none left for legitimate users. This low number of connections allows Slowloris to attack a website without drawing the same attention that a flood of traffic would. There have been 12 identified attacks using the original Slowloris codebase since the BLM website has been protected by Deflect. All but one of these attacks were under 1000 connections. The largest Slowloris attack occurred on July 10th from 0:50 to 3:20 and from 6:00 to 7:20, making over 40,542 connections and clearly misusing this tool or not understanding its original purpose.

In the initial code release Slowloris used a single user agent. Today, many of the custom versions of Slowloris have changed the user agent [pyloris.py] or added source client obfuscation by randomly picking from a list of user agents [slowloris.py]. It is not surprising to see someone using an unmodified version of such an arcane tool even when the server used on the BLM website is protected against that attack. Many of the actors conducting DoS attacks are not building upon existing tools. While Slowloris was elegant at the time, the DoS space is dominated by attackers using simplistic measures. This is because one does not need a highly complex tool to take down most sites on the Internet.

Slowloris attacks on the BLM website have a tendency to overlap with or occur around the time of two low-skill “basic HTTP flood” attacks: [Blank] and [Python], as well as (Blank+WordPress) WordPress attacks.

HTTP Floods

HTTP floods are easy to implement and hard to identify attacks. Generally, they attempt to exhaust a system’s application resources or the network bandwidth. They do this by either creating a large amount of connections to the website or by continuously downloading a large amount of files. Because they only require an attacker to create many legitimate connections to a server, HTTP floods are quite easy to implement. Since these connections are legitimate, it can be very difficult for a defender to differentiate these connections from those of real users.

Simple HTTP Flood

Aliases/Tools None
Attack Type Layer 7 Denial of Service
Exploits None
Obfuscation None
Attack Class Single-source
Attack Rate Low

A simple example of this type of attack can be seen on April 30th. For just under ten minutes one lone address conducted a low sophistication HTTP GET request attack against the Black Lives Matter website. Over a five-minute period this attacker made 1503 connections from a single address using an Internet Explorer user agent. The BLM website only received a few of these connections as the attacker was banned within a second.

 


Basic Python

Aliases/Tools None
Attack Type Layer 7 Denial of Service
Exploits None
Obfuscation None
Attack Class Single-source
Attack Rate Low

Just a few hours after the previous HTTP Flood concluded, two different attacks started and subsided. They missed each other by just two minutes. The first was a “Fully Randomized NoCache Flood” and connected 2,000 times in its two minutes of attack. The second was a test run of an even simpler HTTP Flood attack than the previous example. The code behind this attack was written without any attempt to make it look like a legitimate user. Over the six minute attack, this script made around 400 connections. There were also 23 connection from a Chrome browser at the same IP address during this period, as the attacker frantically refreshed the web page to check on their impact. As in the previous attack, it took under a second for this IP address to be banned.

While a DoS attack does not need much sophistication to be effective, we mention it here because its unique signature shows that this attack was written by an inexperienced programmer. To explain how basic this attack is, the Deflect Labs researchers have recreated a working version of it below.

import urllib
while True:
   urllib.urlopen("http://www.blacklivesmatter.com")

This attacker came back again after a few hours using a different address. As in many single-source attacks, they were likely using a proxy to disguise the original IP of their attacks as they conducted these test runs. Before running the python script, they ran the same “Fully Randomized NoCache Flood” attack for about a minute and then quickly switched back to their python script. The python script made another 429 connections during the approximately six minute long attack. It was, like before, stopped within seconds.

This testing behavior continued over the next few days. With another small attack on the morning of May 1st that made up to 700 connections in just under 10 minutes and one with just over 1000 connections in just under 20 minutes. By the end of that week this attacker had concluded their experiment in attempting to build their own script. Its simple nature made it automatically blocked almost as soon as it connected. At its peak, it could only create a hundred or so connections per minute, which is far too little for a machine conducting a DoS attack.

HTTP Flood DDoS

Aliases/Tools None
Attack Type Layer 7 Denial of Service
Exploits None
Obfuscation None
Attack Class Multi-Source
Attack Rate High

The HTTP floods we have described so far in this section have only come from a single source. In this section we will explore how a botnet can leverage thousands of machines to conduct a distributed HTTP Flood and how we can identify these floods among regular traffic.

HTTP floods that involve many sources (DDoS attacks) are difficult to identify because they can look very similar to regular traffic. But because the BLM website, like every other, has traffic patterns that show the general behavior of their usual readership, there are some clear examples of DDoS HTTP Floods that we can explore.

Unsurprisingly, people in the US visit the BLM website far more often than other groups. This also impacts traffic patterns to the site. Traffic to the BLM website follows a daily cyclical pattern. There is a peak in its traffic between 12:00 and 14:00 EST. (The numbers in our screenshots reflect UTC+0 timestamps.) After that, the traffic slows until around 07:00 EST, when it spikes for the evening and then slows for the night.

Between August 5th and August 9th the hourly pattern changed from a smooth usage pattern like the above into this.

That week between 11:00 and 13:00 EST there was a surge of traffic from China, Indonesia, Turkey, and Slovenia. While the Deflect Labs team is not surprised that BLM receives international attention, it is a bit odd to see it occurring during the same period worldwide. When looking for HTTP Floods that have multiple sources, knowing these usage patterns can make it far easier to identify possible attacks like this one.

The anomaly we can see above was an HTTP POST Flood attack on August 8th. Based upon the dozens of countries per minute that are seen making higher than average connections, it seems plausible that this attack was using a botnet of infected machines.

Over a period of just over an hour, 11,514 machines attempted to upload (POST) a series of large files to the BLM website. This created a flood of large content-length requests that the BLM website had to process.

 



Fully Randomized NoCache Flood

Aliases/Tools Hulk, GoldenEye, BlackHorizon
Attack Type Layer 7 Denial of Service
Exploits KeepAlive, NoCache
Obfuscation Source Client Obfuscation, Reference Forgery
Attack Class Multi-Source
Attack Rate High

Websites that are protected by DDoS mitigation services – such as Deflect – use a “caching” system to store commonly requested pages and provide them to users so the protected website’s server does not have to. This “cache” of recently requested web pages allows Deflect to further limit the requests made to the protected server. Even if simple bots, like those in the last section, evade blocking, they often are just receiving responses from Deflect’s caches of recently requested URLs and not impacting the origin server at all.

DoS providers responded to the use of caching by creating a bot that tricks the cache into thinking that they are requesting a page that was never requested before. These “Cache-bypass” HTTP Floods are bots that add a randomized query at the end of their requests. These randomly generated queries cause a cache to view each request as a new request, even though the bots we are examining in this report only ever requested the main BLM URL “blacklivesmatter.com”.

GoldenEye is a Layer 7 DoS tool. It allows a single computer to open up multiple connections to a website, each of which pretends to be a different device. To do this, GoldenEye provides a different user agent string for each connection. Over the combined hour and a half of attacks, these 11 bots pretended to be hundreds of different types of users to avoid detection.

 

Later in the evening of April 30th another attack consisting of just under 11,000 connections was attempted. This attack used an improved “Fully Randomized NoCache Flood.” While the attack starts with 9 bots using something similar to the code used by GSH, a single address joins a minute into the attack and quickly dwarfs the other attackers in both number of connections and variety of user agents that it employs.

If it were not for the variety of intensity and method used by the individual addresses, attacks like this would look like they involved a single actor. But as is common for attacks against the BLM website, this attack starts slowly with one or two initial actors, who are then joined by a small mob of “bandwagon bullies.” As was seen in the early GSH attacks, they share the tools used in their attacks with the other attackers. Whoever this late attacker is, they are clearly not just another member of the team. This attacker has considerably different network resources and likely software that allows them to have far more impact than all the participating GSH team members.

Reflection DDoS

Joomla! Reflection DDoS

Aliases/Tools DAVOSET, UFONet
Attack Type Layer 7 Denial of Service
Exploits None
Obfuscation None
Attack Class Reflected
Attack Rate High

In 2013 a series of vulnerabilities were discovered in a Google Maps plugin for the Joomla! CMS. One of them made it possible for anyone to request a Joomla! site to make an HTTP request to remote websites. By June 2013 this vulnerability had already been weaponized and included in an existing DDoS framework called DAVOSET (DDoS attacks via other sites execution tool). In 2014 this same vulnerability was included in the UFOnet DDoS framework.

Each of these DDoS frameworks have easy-to-use web-based point-and-click interfaces and a built-in list of vulnerable Joomla! websites. This makes them ideal for a low-resourced or unsophisticated attacker looking to amplify another attack. Of the 23 WordPress attacks made against the BLM website, only 7 of them were not paired with a Joomla! attack.

Initially, it was difficult to identify Joomla! attacks because most of the connections do not provide a user agent string. Empty user agents are somewhat common on the Internet. Many non-malicious, but quickly made, bots do not provide a user agent. As such, when we initially saw these spikes of traffic, we assumed that they were from another sloppily made DoS bot.

After we saw this bot accompanying attacks from a variety of different attackers, we investigated further and noticed a fingerprint hidden in the traffic that led us to the Joomla! attack. Although most of the user agents used by Joomla! sites to make these requests are blank, a small subset of these machines include the version of PHP language that was used to run the request. While blank user agents are somewhat common, many of the attacks that included them were combined with user agents that contained PHP versions. Given the relative rarity of PHP, we realized that the odd increase in empty user agents alongside other attacks was because they were being combined with Joomla! attacks.

 


Introduction to WordPress XMLRPC Floods

Aliases/Tools WordPress Pingback
Attack Type Layer 7 Denial of Service
Exploits NoCache
Obfuscation Spoofing
Attack Class Reflected
Attack Rate High

By default, WordPress has a “pingback” feature that was built to allow WordPress sites to alert other blogs when they linked to their content. On a high level, this works similarly to a mention in Twitter. When a WordPress site publishes a post that links to another website, it sends out a “pingback” to that site with a link to the post containing the original link. If the receiving site is also based on WordPress, it responds to the original site to confirm that it received the pingback.

Pingbacks have been a part of WordPress sites since Version 1.5, which came out in 2005. It wasn’t until 2012 that Christian Mehlmauer released a working implementation of code that took advantage of this feature to ask WordPress sites to verify “pingbacks” from spoofed URLs. Two years later, in March 2014, Akamai released a post that described a “pingback” attack consisting in over 162,000 WordPress sites. In September 2015 they announced that WordPress pingback attacks made up 13% of all Layer 7 attacks they faced.

At 22:00 on May 1st a WordPress pingback attack began targeting the Black Lives Matter website. In just 13 minutes it made 181,301 connections. As this WordPress attack subsided, a Joomla! attack took its place. The moment the WordPress attack started, the second attacker began to use free online services dozens of times a minute to check if the Black Lives Matter website had gone down. As the second attack began, the attacker increased the frequency at which they monitored the state of the website. Four minutes into the attack, when it had obviously succeeded, the attacker stopped checking the site. Altogether, this attack consisted of around 350,000 connections in a period of less than an hour.

 

As was mentioned in the original bulletin, the most intense attacks against the Black Lives Matter website have been WordPress pingback attacks. The first large-scale attack against the BLM website was a WordPress attack on May 9th. This attack made over 1,130,000 connections in just under three hours. It was a mix of over 1,000,000 connections from a WordPress pingback attack alongside 100,000 connections from a “Fully Randomized NoCache Flood.”

 

The following WordPress sections will provide some illustrative examples to show how we explored the relationships between these bots. But we will not examine every attack. Nor will we try to attribute attacks to their source.

WordPress pingback & Botherder Addresses

While WordPress attacks work similarly to Joomla! attacks they are far easier to identify. These attacks clearly list their WordPress version as their user agent. Because these attacks started to become more widespread, a new feature was released in version 3.9 of WordPress. This version updated pingbacks to include the IP address that made the original pingback request.

WordPress/4.6; http://host.site.tld; verifying pingback from 127.0.0.1

We call these IP addresses “botherder” addresses. Some of these addresses correspond to globally addressable IP addresses that one can reach over the Internet. Others are addresses that should never appear on the public Internet. These bogon addresses are private/reserved addresses and netblocks that have not been assigned to a regional Internet registry. The bogon address seen in the example above is called localhost. It’s the IP address used by a computer to refer to itself.

While adding the address of the botherder was implemented to de-anonymize the true source of an attack, most attackers are very adept at concealing their true IP address through the use of spoofed packets, proxies, virtual private servers (VPSs), and the use of compromised machines to conduct the original requests. When we started looking into the botherder addresses, we assumed that we would only find spoofed addresses. To our surprise, the botherder addresses exposed far more than we expected them to. By clustering the botherder IPs exposed in an attack, we were able to develop behavioral profiles that helped us link different attacks together to understand which attacks were likely conducted by the same attacker.

The first thing we looked at were the botherder IPs used in WordPress attacks against the BLM website. Our exploration of bogon addresses showed clear relationships between the attacks that could be exposed by looking at the botherder addresses.

The large blue ball of shared IP addresses on the left side of the bogon graph above surrounds two small incidents that occurred on August 8th and 9th. This massive ball of shared IP addresses consists of over 500 addresses from the private IP address spaces. Specifically, they include 382 addresses from the 172.16.0.0/12 address range and 177 addresses from the 10.0.0.0/8 address range. Private address ranges are not entirely uncommon for WordPress pingback attacks. They can appear when the botherder is on the same hosting provider as the WordPress sites they are exploiting and can also be created when a botherder is spoofing random addresses. What is unusual is how clearly the overlapping bogon IP addresses link these two attacks.

There were also globally addressable botherder IP addresses that linked each of the individual attacks against BLM together. It is likely that areas of sparse overlapping IPs exist because many botherders were clearly spoofing IP addresses. But the areas with many connections showed relationships that were worth exploring.

One commonality between all the attacks was that while every attack has hundreds of spoofed botherder addresses that appear only once or twice, there are also a small number of botherder addresses that account for a majority of the bots herded for the attack. In the August 8th and 9th attacks, which can be seen at the bottom of the globally addressable IP graph, three IP addresses accounted for 95% (13,963 / 14,585) of the WordPress connections that identified a botherder.

Because Deflect’s primary purpose is DDoS mitigation, Deflect Labs’ investigations often happen days or weeks after the fact. This means that we often have to rely on our logs and open source intelligence. In this case one of the first things we looked at was who owned the three primary botherder IP addresses. These IP addresses belong to Digital Ocean, a VPS provider based in New York. Digital Ocean does not provide multiple IP addresses per machine, and so we know that this attack was herded by three separate Digital Ocean “droplets.” Hourly pricing for Digital Ocean droplets runs between $0.007 USD/hour and $0.119 USD/hour. With each of these attacks lasting under half an hour, the cost to run this attack was well below a single dollar.

 


“Bulletproof” Hosting

By far the largest cluster of associated WordPress attacks occurred between July and October. This set of attacks includes the five largest attacks over that four-month period.

Among the 206 globally addressable IPs used by those attacks, 5 botherder IP addresses make up 65% (41,030 / 62,488) of the botherder IPs identified in the attack. Each of these botherders were hosted on an “offshore” hosting provider called DMZHOST. The two most connected botherder IPs in our attacks are mentioned countless times on a variety of IP address reputation websites, forums, and even blog posts as the source of a variety of similar attacks.

“Bulletproof hosting” providers like DMZHOST provide VPSs that advertise themselves as outside of the reach of Western law enforcement. DMZHOST offers its clients “offshore” VPSs in a “Secured Netherland datacenter privacy bunker” and “does not store any information / Log about user activity.” At the same time, DMZHOST’s terms of service are just as concise. “DMZHOST does not allow anything (related) to the following content: – DDos – Childporn – Bank Exploit – Terrorism – NO NTP – NO Email SPAM”.

Conclusion

Silencing online voices is becoming ever easier and cheaper on the Internet. The biggest attacks presented in this report did not require expensive infrastructure, they were simply reflected from other websites to magnify their strength. We are beginning to see authorities pursue and shut down “bulletproof” hosting and booter services that enable a lot of these attacks, yet more needs to be done. In the coming age of IoT botnets, when we begin to witness attacks that can generate over a terabyte of traffic per second, the mitigation community should not guard their intelligence on malicious activity but share it, responsibly and efficiently. Deflect Labs is a small project laying the groundwork for open source community-driven intelligence on botnet classification and exposure. We encourage you to get in touch if you would like to contribute.


 

Deflect is a website security project working with independent media, human rights organizations and activists. It offers DDoS mitigation, secure hosting and attack analytics, free of charge to qualifying organizations. All of our tools are open source and we operate according to strict terms of service and principles promoting the privacy of our clients. Deflect is a project of eQualit.ie, a Canadian not-for-profit organization working to promote and defend human rights in the digital age.

Deflect Labs Report #2

Botnet attack analysis of Deflect protected website bdsmovement.net

This report covers attacks between February 1st and March 31st of six discovered incidents targeting the bdsmovement.net website, including methods of attack, identified botnets and their characteristics. It provides detailed technical information and analysis of trends with the introduction of the Bothound library for attack fingerprinting and botnet classification. We cluster malicious behaviour on the Deflect network to identify individual botnets and employ intersection analysis of their activity throughout the documented incidents and further afield. Our research includes discovered patterns in the selection of targets by the actors controlling these attacks.

Deflect is a website security project working with independent media, human rights organizations and activists. It offers DDoS mitigation, secure hosting and attack analytics, free of charge to qualifying organizations. All of our tools are open source and we operate according to principles promoting the privacy of our clients. Deflect is a project of eQualit.ie, a Canadian not-for-profit organization working to promote and defend human rights in the digital age.

Navigation links: Attack Profile; Botnet profile; Botnet target selection; Botnet behaviour comparison; In-depth incident analysis; Report conclusions

General Info

The Boycott, Divestment and Sanctions Movement (BDS Movement, bdsmovement.net) is a Palestinian global campaign, initiated in 2005. The BDS movement aims to nonviolently pressure Israel to comply with international law and to end international complicity with Israel’s violations of international law. Their website has been protected by Deflect since late 2014 and has frequently been attacked.

Graph 1. Timelion graph showing the average hits per day in the period of February 1 to March 31 (in red) and the moving average + 3 standard deviation (in blue).

Graph 1. Timelion graph showing the average hits per day in the period of February 1st to March 31st (in red) and the moving average + 3 standard deviation (in blue).

Attack Profile

During February and March of 2016, there were 6 recorded incidents against the target website. The Deflect Labs infrastructure allows us to capture, process and profile each attack, analysing unique incidents and intersecting findings with a database of profiled botnets. We define the parameters for anomalous behaviour on the network and then group (“cluster”) malicious IPs into botnets using unsupervised machine learning algorithms.

Graph 2. Total hits to the website, by country of origin. The spikes represent attacks investigated in this report

Graph 2. Total hits to the website, by country of origin. The spikes represent attacks investigated in this report

Graph 3. Prevalence of WordPress pingback attacks during the six incidents

Graph 3. Incidents where the WordPress pingback attack is used against the target site

We define each incident by wrapping it inside a given time frame, record the total number of hits that reached the website during this time and use our analytic tool set to separate malicious requests made by bots from genuine everyday traffic.

Table 1. Attacks Summary, including start/end date, duration, size of the incident, size and number of the botnets detected

id Incident Start Incident Stop Duration Total hits Unique IPs No. of bots identified Identified botnets
29 2016-02-10 21:00 2016-02-11 01:00 ~5hrs 879,634 14,773 12,921 3
30 2016-02-11 10:30 2016-02-11 12:30 ~2hrs 321,203 11,108 9,023 3
31 2016-03-01 15:00 2016-03-01 19:30 ~6h30 3,597,689 5,918 3,243 3
32 2016-03-02 12:30 2016-03-02 16:00 ~3h30 13,559,169 19,851 2,748 2
33 2016-03-04 09:00 2016-03-04 09:30 ~30min 2,058,710 9,613 8,844 1
34 2016-03-08 14:20 2016-03-08 16:40 ~2h20 5,017,045 7,937 7,151 1

The number of unique bots and their grouping into specific botnets is the result of clustering work by BotHound. This toolkit classifies IPs by their behaviour, and allows us to determine the presence of different botnets in the same incident (attack).

Botnet profile

Using BotHound, we have calculated the percentage of unique IPs (classified as bots) that recur in separate incidents. A substantial percentage of previously seen bots would be one way to identify whether a botnet was re-used for attacking the same target. It would reveal a trend in botnet command and control behaviour. This intersection of botnet IPs also creates an opportunity to compare activity between several target websites, whether protected by Deflect or on one of our peers’ networks. Taken together, we begin to build a profile of activity for each botnet, helping us make assumptions about their motivation and target list.

Table 2. Intersection of identical bots across the incidents

Incident #

No. of identical bots
in both incidents

The portion of identical bots
(of the smallest incident)

29, 30 6,928 76.8%
31, 32 1,450 91.0%
33, 34 4,249 59.4%
32, 33 438 17.9%
Graph xx. Hits from bots, by the identified botnet, by the country of origin

Graph 4. Hits from bots and their country of origin, grouped by identified botnets. Update your software and malware cleaners please!

Table 3. Identified botnets and the incidents they appear in

Botnet ID Seen in incident Unique bots Top 10 countries of bot origin Attack method
1 29, 30 13,857 Russian Federation; Ukraine; China; Lithuania; Germany; Switzerland; Gibraltar; United Kingdom; Netherlands; France POST
2 29, 30 8,913 Russian Federation; China; Ukraine; Germany; Lithuania; United States; Switzerland; United Kingdom; France; Gibraltar POST
4 31, 32 2,589 United States; Germany; United Kingdom; Netherlands; China; Japan; Singapore; Ireland; France; Spain; Australia Pingback
5 31, 32 772 United States; United Kingdom; Germany; Netherlands; Italy; France; Russian Federation; Singapore; Canada; Japan; China Pingback
6 31 971 United States; China; Germany; Japan; United Kingdom; Singapore; Netherlands; France; Ireland; Canada; Australia Pingback
7 33, 34 11,746 United States; United Kingdom; Germany; France; Netherlands; China; Canada; Russian Federation; Ireland; Spain; Turkey Pingback

Botnet target selection

Deflect protects a large number of qualifying human rights and independent media websites the world over. Our botnet capture and analytic tool set allows us to investigate attack characteristics and patterns. We consider the presence (intersection) of over 30% of identical bots as originating from a similar botnet. During our broader analysis of the time period covered by this report, we have found that botnet #7, which targeted the bdsmovement.net website on March 3rd, also hit the website of an Israeli Human Rights organisation under our protection on April 5th and April 11th. In each incident, over 50% of the botnet IPs hitting this website were also part of botnet #7 analysed in this report. Furthermore, a peer website security organization reviewed our findings and concluded that a substantial amount of IPs belonging to this botnet were targeting another Israeli media website under its protection, on April 7th and April 12th. Organisations targeted by this botnet do not share a common editorial or are in any way associated with each other. Their primary similarities can be found in their emphasis on issues relevant to the protection of human rights in the Occupied Territories and exposing violations in the ongoing conflict. Our analysis shows that these websites may have a common adversary — the controller or renter of botnet #7 — that their individual work has aggrieved. We will present our findings on this investigation in more detail in an upcoming report.

Botnet behaviour comparison

BotHound works by classifying the behaviour of actors on the network (whether human or bot) and clustering them according to a set of pre-defined features. Malicious behaviour stands out from the everyday trend of regular traffic. On the picture below the RED spots refer to attacker sessions, while BLUE spots refer to all other (regular traffic). The graphic displays all the 6 incidents combined. We chose the following 3 dimensions to visually represent a projection from a 7-dimensional space (where BotHound clustering is calculated):

  • HTTP request depth
  • Variance of HTTP request interval
  • HTML to image ratio

Graph 5. Clustering of bot behaviour from the six incidents covered in this report. The graphic illustrates that malicious behaviour, no matter the botnet characteristics, follows a determined pattern which resembles automated machine-driven properties of a botnet attack.

In-depth incident analysis

We have captured, analysed and now profiled each botnet witnessed in the 6 incidents. We break down incidents into three groups, by similarity of attack characteristics and the time of occurrence.

Incidents #29 & #30

Date: February 10-11, 2016
Duration: approximately 28 hours
Identified botnets: 2 (botnet id: #1 #2)
IP intersection between botnets: 76%
Attack type: HTTP POST


image11

Attack analysis

After doing extensive cluster analysis to separate “good” from “bad” IPs based on their behaviour during the incident time frame, we applied a novel secondary clustering method which identified two different patterns of behaviour spanning both incidents. The first attack pattern was using bots to hit the target very fast, with similar characteristics (session length, request intervals, etc.). The second botnet was hitting slower, but more consistently. The session length was varying, likely to evade our mitigation mechanisms. However, the request interval between hits was zero, which helped us identify them. It is easy to distinguish two different botnets from the graphs below.

Identified botnet #1
Members: 13,857
Observations:

  • Session length = 314 sec
  • Payload average = 521 byte
  • Hit rate = 0.04 /minute
  • Requests: 500,000
  • Host header: accurate
  • Method: POST (> 99.9%)
  • URI path: / (> 99.9%)
  • UA: low variation, with most major UAs represented

Deflect Response: Moderate blocking success, origin was affected.


Identified botnet #2
Members: 8,913
Observations:

  • Session length = 429 sec
  • Payload average = 447 byte
  • Hit rate = 0.05 /minute
  • Requests: 600,000
  • Host header: accurate
  • Method: POST (> 99.9%)
  • URI path: / (> 99.9%)
  • UA: low variation (slightly higher than botnet 1), most major UAs represented

Deflect Response: Moderate blocking success, origin was affected.

Attacks results primarily in response code 502 (bad gateway) and 504 (gateway timeout) codes.

The botnet utilises several hundred unique IPs and a few dozen rotating user agents

The botnet attacks with several hundred unique IPs (purple) and rotates through a few dozen user agents (yellow)

The botnet attacks with several hundred unique IPs and rotates through a few dozen user agents. Graph tallies at 15 second intervals.

IP geo-reference

The IP address requesting a site can be geo-located. Another way we visualize botnet behavior is by cross-referencing the country of bot origin. We can easily see attack intensity (number of hits) versus bot distribution (unique IPs) in the diagrams below.

Graph 6. Hits against target website, by their geographic origin.

Graph 6. Hits against target website, by their geographic origin.

Graph 7. The same timespan as per the previous graph, only this time showing a count of unique IPs, per country geoIP

Graph 7. The same timespan as per the previous graph, only this time showing a count of unique IPs, per country geoIP

User agent and device

Every website request usually contains a header with identifying information about the requester. This can be faked, of course, but in any case stands out from the general pattern of traffic to the website. These incidents had a high consistency of “Generic Smartphone” and “Other” devices – describing the hardware unit from which the request was supposedly made. It is common for botnets to spoof a user agent device or, at least, share a common one.

Graph 8. Shows the devices used in the February botnet attack. As we can see, the majority comes from “Generic Smartphone” or “Other” device. Such consistency shows that these are part of an attack, rather than regular visitors.

Graph 8. Shows the devices used in the February botnet attack. As we can see, the majority comes from “Generic Smartphone” or “Other” device. Such consistency shows that these are part of an attack, rather than regular visitors.

Conclusions on incident #29 and #30 attacks
  • These attacks were distinguished by the relatively large number of participating bots, but were smaller in intensity (number of hits on target) compared to incidents #31-34. Three attacks were launched during the period of these incidents, requesting the same url ( /- ), as well as using the same “device” in the user agent of the request.
  • There were two and possibly three botnets in these incidents. They can be differentiated by the geographic location of their bots and hit rates during attack. What is interesting is that the attack method between the different botnets and attack times is the same. Also the two botnets share a high percentage of intersecting bot IPs (76.8%). This may be an indication that they are subnets of a larger malicious network and are being controlled by the same entity.

Incidents #31 & #32

Date: March 1-2, 2016
Duration: approximately 21.5 hours
Identified botnets: 3 (botnet id: #4 #5 #6 )
IP intersection between botnets: 91%
Attack type: Reflection – WordPress Pingback[1]

Attack Analysis

Attackers utilised the same botnet (91% intersection) during incidents #31 and #32 within a time range of 22 hours. Incident #32 is the biggest in terms of hits out of the entire period covered by this report – counting over 13.5 million total hits in 6 hours. These incidents have a very similar UA (device) characteristic, the majority of which are identified as “Spider” (we are making an intersectional analysis on the UA further down in this report).

Identified botnet #4

Members: 2,589
Observations:

  • Session length = 2,971 sec
  • Payload average = 8,217 byte
  • Hit rate = 1.7 /minute
  • Requests: 10.8 million
  • Host header: accurate
  • Method: GET (> 99%)
  • URI path: / (> 99%)
  • UA: high variation, all WordPress pingback

Deflect Response: Successfully blocked. 91% of responses to botnet processed by edge within 20ms

Comparison of unique IPs versus unique user agent strings at 30 second intervals

Comparison of unique IPs versus unique user agent strings at 30 second intervals

Identified botnet #5
Members: 772
Observations:

  • Session length = 3,587 sec
  • Payload average = 10,221 byte
  • Hit rate = 0.48 /minute
  • Requests: 3 million
  • Host header: accurate
  • Method: GET (> 99%)
  • URI path: / (> 99%)
  • UA: high variation, all WordPress pingback

Deflect Response: Successfully blocked. 85% of responses to botnet processed by edge within 20ms

Comparison of unique IPs versus unique user agent strings at 30 second intervals. Note the probing attack before escalation

Comparison of unique IPs versus unique user agent strings at 30 second intervals. Note the probing attack before escalation

Identified botnet #6
Members: 971
Observations:

  • Session length = 583 sec
  • Payload average = 31,317 byte
  • Hit rate = 0.49 /minute
  • Requests: 145,000
  • Host header: accurate
  • Method: GET (> 99%)
  • URI path: / (> 99%)
  • UA variation: high variation, all WordPress pingback

Deflect Response: Relatively small incident – some attackers did not trigger our early detection with around 15% getting through to origin (22,000 requests returned an HTTP 200). Successfully blocked.

Error codes showing blocked request versus those that got to the origin site in incident #31

Error codes showing blocked request versus those that got to the origin site in incident #31

User agent and device

The “UA” parameter in our logging system identifies the user agent string in the request header made to the target website. It usually represents the signature (or version) of the program used to query the website, for example “Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko” means that the request was made from Internet Explorer version 11, running on the Windows 7 operating system [2]. The “device” parameter in our logging system identifies the hardware (device) the user agent is running on, for example “iOS Device” or “Nexus 5” or “Windows 7”. In this case, the vast majority of IP addresses hitting the site were categorised as “spiders”. A spider, or web crawler, is software used by search engines to index the web. User agent strings are just text and can be changed (faked) to say anything – including copying a user agent string commonly used by some other software.

Graph 9. Hit count from various devices throughout incidents 31-32

Graph 9. Hit count from various devices throughout incidents 31-32

Graph 10. Unique IP count from various devices throughout incidents 33-34

Graph 10. Unique IP count from various devices throughout incidents 33-34

Conclusions on incident #31 and #32 attacks
  • These incidents stand out for their common attack and attacker characteristics, with an intersection of 91% of bots used in both instances (of the smaller incident). Botnet #4 and #5 behaviour differs only in their hit rate. Botnet #5 and #6 have a similar number of bots and an almost identical hit rate. Interestingly, they differ greatly in the number of hits each one of them launched at the target site. It seems that all three botnets had strong presence on computers in the United States. All botnets used the same attack method – WordPress pingback – in both incidents.
  • The similarities between bot IP addresses and the attempts to vary the attack pattern from very similar botnets indicates human lead efforts to adapt their botnet to get past Deflect defences. It appears that the botnets used in these two incidents have the same controller behind them.

Incidents #33 & #34

Date: March 4, March 8, 2016
Duration: 30 mins, 2 hours and 20 minutes
Number of bots: 8,844 and 7,151
Identified botnets: 1 (botnet id: #7)
Attack type: Reflection – WordPress Pingback[1]


Identified botnet #7
Members: 11,746
Observations:

Graph XX. Comparable values of unique IPs and unique UAs. We see a huge difference from other botnets in this report

Graph 11. Comparable values of unique IPs and unique UAs. We see a huge difference from other botnets in this report

  • Session length = 2,665 sec
  • Payload average = 15,572 byte
  • Hit rate = 0.30 /minute
  • Requests: 7.9 million
  • Host header: accurate
  • Method: GET (> 99%)
  • URI path: / (> 99%)
  • UA variation: high variation, mostly WordPress pingback (92%)

Deflect Response: Moderate blocking success. 75% of requests dealt with in <200ms, 5% origin read timeouts

Graph 10. Unique IP count from various devices throughout incidents 33-34

Graph 12. Unique IP count from various devices throughout incidents #33-34

Conclusions on incident #33 and #34 attacks
  • Incident #33 comes across as a probe (or a first attempt) before a much stronger attack with similar characteristics is launched in incident #34. This is backed up by the use of a single botnet in both incidents.
  • Botnet #7 appears in other attacks against Israeli websites, on our network and on the network of one of our peers. The attack pattern used in these incidents is similar to the previous two incidents, and we have found a 17.9% intersection between bots used in incidents #32 and #33, possibly linking #31-34 together. Along with the prevalence of bots originating from the United States, there is some justification that botnets 4-7 originate from a similar larger network.

Report conclusions

Attempts to bring down the bdsmovement.net website were made using several (at least two distinct and relatively large) botnets and varied in their technical approach. This shows a level of sophistication and commitment not generally seen on the Deflect network. The choice of attack method allowed us to see which website was being targeted, which may have been a conscious decision. However, we did not find anything linking attacks in incidents #29-30 with attacks in incidents #31-34. Relative success with affecting the origin in the first two incidents was not built upon in the next four. Furthermore, other effective methods to swarm the network with traffic or overwhelm our defence mechanisms could have been used, had the attackers had enough resources and dedication to achieve their aims.

The creation of historical profiles for botnet activity and the ability to intersect our results with peer organizations will lead to better understanding of trends, across a greater swath of the Internet. Adapting botnet classification tooling to automated defense mechanisms will allow us to notify peers about established and confirmed botnets in advance of an attack. By slowly chipping away at the impunity of botnet controllers, we hope to reduce the prevalence of DDoS attacks as a method for suppressing online voices.

eQualit.ie is inviting organizations interested in this collaboration to reach out.

 



[1] A WordPress pingback attack uses a legitimate function within WordPress, notifying other websites that you are linking to them, in the hope for reciprocity. It calls the XML-RPC function to send a pingback request. The attacker chooses a range of WordPress sites and sends them a pingback request, spoofing the origin as the target website. This feature is enabled by default on WordPress installations and many people run their websites unaware of the fact that their server is being used to reflect a DDoS attack.
[2] http://www.useragentstring.com/index.php

Deflect stats March 2016

This is the first in a monthly series of posts sharing and discussing statistics on the Deflect network. March 2016 was a busy month for us. We began to publish analytic reports on DDoS attacks against some of the clients we protect on the network. Our aim is to help the target’s advocacy efforts and begin strip away at the impunity currently enjoyed by botnet operators. As our analytic tooling and understanding of these attacks improve, so will the reports.

In terms of people served and traffic on the network, this was our busiest month to date. We averaged around 20 million daily hits, a significant percentage of which came from readers in Mexico. Around ten separate DDoS incidents were recorded during the month, of various strength and sophistication.

 

Total hits this month, unique IPs we banned; unique IPs we served

Total hits this month, unique IPs we banned; unique IPs we served

 

Daily hits on the Deflet network

Daily hits on the Deflect network

 

Daily count of unique IPs by country of origin

Daily count of unique IPs by country of origin

 

This month's share of unique IPs by country of origin is a tortilla!

This month’s share of unique IPs by country of origin is a tortilla!

 

Most popular operating systems on the network

 

Attacks on the Deflect network in March 2016

Around a dozen separate incidents were recorded on the network in March. It’s important to note that these are requests that triggered our banning mechanisms. In reality there may have been many more malicious requests.

IP-Bans_by_country_date_histogram

Daily unique IP bans by country

 

Geographical bot distribution

Geographical bot distribution

We are also beginning to track botnets as anomalies on the network. Herein a graph built using the Timelion toolkit for ElasticSearch. It consists of time-series based representation of total hits on the network (red line) and a moving average (blue line) – specific browsing patterns as generated by readers behavior week upon week. We then multiply the blue line values by 3 so we can clearly see when an anomaly is happening on the network. Most of the time, although not every-time, the anomaly represents a spike in traffic or hits on websites – an attack.

timelion

We have also been contributing towards the development of a tool called GreyMemory. It is an anomaly detection tool which accepts any multi-dimensional time series as input, then predicts the next state of the system, measures the error of prediction and generates an anomaly rate. It uses predictive algorithms to evaluate what might happen next on the network, and compares this evaluation with the eventual result. If the quality of prediction drops, it alerts the anomaly. On the following diagram GRAY is the ratio of successful HTTP requests divided by the total # HTTP requests; BLUE is the anomaly rate, as calculated by GreyMemory and ORANGE is the anomaly Alert, where we should create incidents. Alerts are triggered when anomaly rate exceeds a threshold, which is currently on 95%

GreyMemoryReportMarch2016_2

Deflect Labs Report #1

Botnet attack analysis covering reporting period February 1 – 29 2016
Deflect protected website – kotsubynske.com.ua

This report covers attacks against the Kotsubynske independent media news site in Ukraine, in particular during the first two weeks of February 2016. It details the various methods used to bring down the website via distributed denial of service attacks. The attacks were not successful.

General Info

Kotsubynske is a media website online since 2010 created by local journalists and civil society in response to the appropriation and sale of public land (Bylichaniski forest) by local authorities. The website publishes local news, political analysis and exposes corruption scandals in the region. The site registered for Deflect protection during an ongoing series of DDoS attacks late in 2015. The website is entirely in Ukrainian. The website receives on average 80-120 thousands daily hits, primarily from Ukraine, the Netherlands and the United States.

 

image1

Attack Profile

Beginning on the 1st of February, Deflect notices a rise in hits against this website originating primarily from Vietnamese IPs. This may be a probing attack and it does not succeed. On the 6th of February, over 1,300,000 hits are recorded against this website in a single day. Our botnet defence system bans several botnets, the largest of which comprises just over 500 unique participants (bots).

Using the ‘Timelion’ tool to detect time series based anomalies on the network, such as those caused by DDoS attacks, we notice a significant deviation from the average pattern of visitors to the Kotsubynske website (on the diagram below, hits count on the website are in red, while the blue represents a 7-day moving average plus 3 times standard deviation, yellow rectangles mark the anomalies). The fact that the deviation from the normal is produced over a week (Feb 1 to Feb 8) points to the attack continuing over several incidents. This report attempts to figure out whether these separate attacks are related and display attack characteristics and makes assumptions about its purpose and origin.

 

Illustration 1: Timelion graph showing a prolonged attack

Illustration 1: Timelion graph showing a prolonged attack period between February 1 and 8

February 06, 2016 Attack profile

This incident lasted 1h 11min and was the most intensive attack during this period, in terms of hits per minute.

Incident statistics
Here are listed part of the incident statistics that we get from the deflect-labs system. They show the intensity of the attack, the type of the attack (GET/POST/Wordpress/other), targeted URLs, as well a number of GEOIP and IP information related to the attacker(s):

  • client_request host:”www.kotsubynske.com.ua”
  • Hits between 24000 and 72000 per minute
  • Total hits for the attack period: 1643581
  • Attack Start: 2016-02-06 13:34:00
  • Attack Stop: 2016-02-06 14:45:00
  • Type of attack: GET attack (bots requested page from website)
  • Targeted URL: www.kotsubynske.com.ua
  • Primary botnet request: “http://www.kotsubynske.com.ua/-”
Illustration 2: Geographic distribution of bots

Illustration 2: Geographic distribution of bots

The majority of hits on this website came from Vietnam, Ukraine, India, Rep of Korea, Brazil, Pakistan. Herewith are the stats for the top five countries starting with the most counts and descending:

geoip.country_name Count
Vietnam 817,602
Ukraine 216,216
India 121,405
Romania 70,697
Pakistan 61,201

 

Cross-incident analysis

We’ve researched three months of incidents on the Kotsubynske website, namely from January to March 2016. We have detected five incidents between February 01 – 08 and present a detailed analysis of botnet characteristics and the similarities between each incident. The point is to figure out if the incidents are related. This may help us define whether the actors behind this attack were common between all incidents. For example, we see relatively few IPs appearing in more than one incident, while each incident shares a similar botnet size and attack pattern.

 

Illustration 3: GeoIP location of bots over the 5 incidents

Illustration 3: GeoIP location of bots over the five recorded incidents

 

Table 1. Identical IPs across all the incidents

We identify, in sequence of incidents, botnets IPs which re-appeared from a previous attack.

ID Incident start Incident end Duration botnet IPs Recurring botnet IPs Attack type Attack pattern (URL request)
1 2016-02-02 12:0700 2016-02-02 12:21:00 14 min 224 GET 163224 hits: /-
2 2016-03-02 08:27:00 2016-03-02 08:31:00 4 min 120 22 GET 35991 hits: /-
3 2016-05-02 21:10:00 2016-05-02 22:00:00 50 min 99 0 GET 49197 hits : /-
23 hits: /wp-admin/admin-ajax.php
4 2016-06-02 13:34:00 2016-06-02 14:45:00 1h 11 min 484 0 GET 1557318 hits: /-
5 2016-08-02 12:20:00 2016-08-02 16:40:00 4 h 20 min 361 0 GET 392658 hits: /-

 

Table 2. Pairs of incidents with significant numbers of identical IPs banned by Deflect

Here we correlate each incident against all other incidents to see whether any common botnet IPs reappear and present the incident pairs where there is a match

incident id banned IPs incident id banned IPs recurring IPs % of recurring botnet IPs
in the smaller incident
1 224 2 120 22 18.3%
3 99 4 484 15 15.2%

Analysis of the five attacks shows thats very few botnet IPs were reused in subsequent attacks. The presence of any recurring IPs however suggests that they either belong to a subnet of the same botnet or are victims whose computers have been infected by more than one botnet malware. Furthermore, each botnet’s geoIP characteristics and behaviour is almost identical. For example, whilst traffic during this period followed the normal trend, both in terms of number of visitors and their geographic distribution, banned IPs were primarily from Vietnam, India, Pakistan and other countries that do not normally access kotsubynske.com.ua

This is a reliable indicator of malicious traffic and a transnational botnet.

  • 71.1% of banned IPs come from Vietnam, India, Iran, Pakistan, Indonesia,Saudi Arabia, Philippines, Mexico, Turkey, South Korea.
  • 99.9% of banned IPs have identical user agent string: “Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; WOW64; Trident/6.0)”.
  • The average hit rate of IPs with the exact identical user agent string is significantly higher: 61.9 hits/minute vs 4.5 hits/minute for all other traffic.
Illustration 4: Banned machines from 'unusual' countries

Illustration 4: Banned machines from ‘unusual’ countries for kotsubynske.com.ua

The user agent (UA) string seems to be identical in all five incidents, when comparing banned and legitimate traffic. In the diagram below, Orange represents the identical user agent string, whilst blue represents IPs with other user agent strings. The coloured boxes contain 50% of IPs in the middle of each set and the lines inside the boxes indicates the medians. The markers above and below the boxes indicate the position of the last IP inside 1.5 height of the box (or inside 1.5 inter quartile range).

Illustration 5: Hit rate distribution for the IPs with the same identical user agent string

Illustration 5: Hit rate distribution for the IPs with the same identical user agent string: “Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; WOW64; Trident/6.0)”

Even though there are not many identical botnet IPs across all of the 5 incidents, the behaviour of botnet IPs from different incidents is very similar. The figure below illustrates some characteristics of the botnet (different colours) in comparing with regular traffic (blue colour).

Scatter plot of sessions in 3-dimensional space:

  • Request interval variance
  • Error rate
  • HTML to image ratio

image7

Report Conclusion

On the 2nd of February, the Kotsubynske website published an article from a meeting of the regional administrative council where it stated that members of the political party ‘New Faces’ were interfering with and trying to sabotage the council’s work on stopping deforestation. The party is headed by the mayor of the nearby town Irpin. Attacks against the website begin thereafter.

Considering the scale of attacks often witnessed on the Deflect network, this was neither strong nor sophisticated. Our assumption is that the botnet controller was simply cycling through the various bots (IPs) available to them so as to avoid our detection and banning mechanisms. The identical user agent and attack pattern used throughout the five attacks is an indication to us that a single entity was orchestrating them.

This is the first report of the Deflect Labs initiative. Our aim is to strip away the impunity currently enjoyed by botnet operators the world over and to aid advocacy efforts of our clients. In the near future we will begin profiling and correlating present-day attacks with our three year back log and with the efforts of similarly minded DDoS mitigation efforts.

Deflect Labs – fighting impunity with analytics and advocacy

For the last four years, the Deflect DDoS mitigation system has protected independent online voices from the onslaught of cyber-attacks aiming to silence them. We have grown, learning our lessons as we took the punches. One aspect of this work stood out as particularly interesting during this time: there were stories to be told in the sea of data brought on by each attack. Those stories could shine a light in the direction of the provenance of the attacks and the motivations of the actors behind them. Most importantly, it would aid the advocacy efforts of the targeted website and begin to strip away the impunity for launching these attacks, raising their cost in the long run. The more they attack us, the smarter we’ll get.

Deflect Labs is a new effort to collect and study distributed denial of service (DDoS) attacks launched against the websites we protect. It is built on a variety of open source tools, utilizing machine learning, time-series anomaly detection and botnet classification tools, many of which have been contributed to or wholly developed by eQualit.ie’s Deflect team. We aim to responsibly share news and our analysis of the attacks in a series of ongoing reports, the first of which is released today.

infogram