Giant leaps in our machine-lead mitigation tooling have removed some of the heavy load in mitigating attacks from our support team this month. We’re very pleased with the machine’s performance but it won’t replace the humans! Below, we share some traffic highlights, Deflect relevant events and stories from our clients.
January Traffic
Throughout the month of January, Deflect served over 884 million requests to more than 9 million unique readers around the world. Much of the traffic was bound for Los Danieles – a new and very popular independent media publication in Colombia. Every Sunday, their website attracts between 5 and 9 million legitimate hits! Thankfully, Deflect was able to serve over 94% of these requests directly from its edge cache.
Another notable traffic event this month coincided with the release of an online report, investigating torture of thousands of Belarus protesters at the hands of the incumbent government forces. Published by the renowned Committee Against Torture, this is a visual investigation, suitable for mature audiences only.
Notable Attacks in January
Sixteen distinct attacks were recorded against Deflect protected websites this month. Of these, five were notable for their strength and consistency, with two attacks continuing over a four-day period. The largest attack, with over 5000 bots participating, was targeting the Vietnamese independent new site Tiếng Dân. This is not the first time their website has been targeted. Approximately half of the attacking bots were discovered and challenged by Baskerville, whilst the other half were blocked by our manual rule sets. Overall, Deflect maintained 100% network up-time in January.
Kandinsky theme options for eQpress
Deflect clients who run or would like to migrate to our secure WordPress hosting platform can now request the installation of a new theme called Kandinsky. Developed by our friends at «Теплица социальных технологий» (Greenhouse for Social Technology) in response to needs expressed by civil society groups, wishing to have an effective and well designed online presence Kandinsky offers three templates and guides website creators with helpful tips and check lists. You can read our full interview with Kandinsky here.
Deflect and the World Social Forum
On January 29th, Deflect staff participated in a live panel during the World Social Forum together with our partners Colnodo and the Foundation for Freedom of the Press (FLIP). Julian Casasbuenas from Colnodo presented the use case of a Colombian independent media site losdanieles.com as an example of Deflect protection and its importance to the development of free journalistic expression in Colombia. The losdanieles.com project was launched by a group of highly reputable journalists who had been implicated in the Colombian parapolitics scandal.
To finish this newsletter, we wanted to share a lovely thank-you video sent to us by LosDanieles.com columnist Daniel Samper Ospina.
Find the Daniels on Twitter @DanielSamperO, @DanielsamperP and @DCoronell
This is the fifth year of Deflect operations and an opportune time to draw some conclusions from the past and provide a round of feedback to our many users and peers. We fought and won several hundred battles with various distributed denial of service and social engineering attacks against us and our clients, expanding the Deflect offerings of open source mitigation solutions to also include website hosting and attack analytics. However, several important missteps were taken to arrive here and this post will concentrate on lessons learned and the way forward in our battle to reduce to prevalence of DDOS as an all too common technique to silence online voices.
Our reflections and this post were motivated by an external evaluation report of the Distributed Deflect service, which you can read in this PDF. The project itself was a technical long shot and an ambitious community building exercise. Lessons learned from this endeavor are summarized within. Its about a 10 minute read 🙂
During peak times on Deflect throughout 2012-2016 we were serving an average of 3 million unique daily readers and battling with simultaneous DDoS attacks against several clients. The network served websites continuously for the entire 3 1/4 years of project duration, recording less than 30 minutes of down time in total. The project had direct impact on over four hundred independent media, human rights and democracy building organizations.
Over three hundred and fifty websites passed through the Deflect protection service. These websites ranged in size and popularity, receiving anything between a dozen daily readers to over a million. Our open door policy meant that websites who had changed their mind about Deflect protection were free to leave and unhindered in any way from doing so. Over the course of the project, we have mitigated over four hundred DDoS attacks and served approximately 1% of Internet users each calendar year (according to our records correlated against Internet World Statistics). Our work also appeared in topical and mainstream media.
Aside from the DDoS protection service, we trained numerous website administrators in web security principles, worked with several small and medium ISPs to set up their own Deflect infrastructure and enabled Internet presence for key organizations and movements involved in national and international events, including the ’13 election in Iran, ’14 elections in Ukraine, Iguala mass kidnapping, Panama papers, and Black Lives Matter among others.
Distributed Deflect
As attacks grew in size, we debated the long-term existence of the project, deciding to prototype an in-kind DDoS mitigation service, whereby websites receiving free protection and any volunteers could join and expand the mitigation network’s size and scope. We wanted to create a service run by the people it protected. The hypothesis envisioned the world’s first participatory botnet infrastructure, whereby the network would be sustained with around a hundred servers run by the Deflect project and several thousand volunteer nodes. Our past experience showed that the best way to mitigate a botnet attack was with a distributed solution, utilizing the design of the Internet to nullify an attack that any single end point/s could not handle by itself. Distributed Deflect brought together people of various background and competencies, blending software development and technical service provision, customer support and outreach, documentation and communications. We designed, prototyped and brought into production core components of a distributed volunteer infrastructure, only to realize that the hypothesis behind our proposal could not scale if we were to maintain the privacy and security of all participants in our network.
An infrastructure that would accept voluntary (untrusted) network resources had to introduce checks for content accuracy and confidentiality, otherwise a malicious node could not only see who was doing what on the Deflect network but delete or change content as it passed through their machine. Our solution was to encrypt web pages as they left the origin server and deliver them to readers as an encrypted bundle, with an additional authentication snippet being sent by another node for verification. Volunteer nodes would only be caching encrypted information and would not be able to replace it with alternative content.
All necessary infrastructure design and software tools to implement this model were built to specification. However, once ready for production and undergoing testing, we realized the error in hypothesis made at the onset. Encrypted bundles grew in size, as all page fonts and various third-party libraries – that make up the majority of web pages today and are usually stored in the browser’s cache – had to be included in each bundle.
This increased network latency and could not scale during a DDoS attack. We were worsening the performance of our infrastructure instead of improving it. Another important factor driving our deliberation was the low cost of server infrastructure. By renting our machines with commercial providers, and using their competitive pricing to our advantage, we have managed to maintain infrastructure costs below 5% of our overall monthly expenditure. Monetary support for a worldwide infrastructure of Deflect servers was not significant when compared with the resources required to service the network. By concentrating development efforts on encrypting and delivering website content from our distributed cache and performance load balancing on a voluntary node infrastructure, we held back work on improving network management and task automation. This meant that the level of entry to providing technical support for the network was set quite high and excluded the participation of technically minded volunteers protected by Deflect.
After several months of further testing, deliberation and consultation with our funders, we decided to abandon the initiative to include voluntary network resources, in favour of continuing the existing mitigation platform and improving its services for clients. As attack mitigation became routine and Deflect successfully defended its clients from relentless DDoS offensives, the team began to look at the impunity currently enjoyed by those launching the attacks. Beginning with a case of a Vietnamese independent media website targeted by bots originating from a state-regulated and controlled Vietnamese ISP, we understood that a story could be extracted from the forensic trail of an attack, that may contain evidence of motivation, method and provenance. If this story could be told, it would give huge advocacy power to the target and begin to peel away at the anonymity enjoyed by its organizers. The cost for attacking Deflectees would raise as exposure and media attention around the event upended the attackers’ goals.
We began to develop an infrastructure that would capture a statistically relevant segment of an attack. Data analysis was achieved through machine-led technology for profiling and classifying malicious actors on our network, visualization tools for human-led investigation and cooperation with peer organizations for tracing activity in our respective networks. This effort became Deflect Labs and in its first twelve months we published three detailed reports covering a series of incidents targeting websites protected by Deflect, exposing their methodology and profiling their networks. Doing some open source intelligence and in collaboration with website staff, we identified a story in each attack exposing possible motivations and identity of the attackers. Following publication and media attention created by these reports, attacks against one of the websites reduced significantly and ceased altogether for the other one.
Bot behavior follows a certain pattern inside the seven dimensional space create by Bothound analytics
Challenges
Many difficulties and problems could be expected with running a high-impact, 24/7 security service for several million daily readers. Fatigue, lack of time for developing new features, round-the-clock emergency coverage and numerous instances of high-stress situations led to burnout and staff turnover. The resources invested in the Distributed Deflect model set back development considerably for other project ambitions. At around the same time as Deflect was gaining popularity, free mitigation offerings from Cloudflare and Google were introduced in tandem with outreach campaigns targeting independent media and human rights organizations. This led to more options for civil society organizations seeking website protection but made it harder for us to attract the expected number of websites. We started a campaign to define differences in our distinctive approaches to client eligibility, respect for their privacy and clear terms of service, trying a variety of communications and outreach strategies. We were disappointed nonetheless to not have received more support from within our community of peers, as open source solutions and data ownership did not figure highly as criteria for NGOs and media when selecting mitigation options.
… we carry on
Deflect continues to operate and innovate, gradually growing and solidifying. Our ongoing ambitions include offering our clients broader hosting options and coming up with standards and systems for responsible data sharing among like-minded ISPs and mitigation providers. Look out for pleasant graphic user interfaces in our control panels and documentation platforms. We are also prototyping several different approaches to generating revenue in order to sustain the project for the foreseeable future. The goal is to get better without losing track of what we came here to do in the first place. As always, we are here to support our clients’ mission and their right to free expression. We are heartened by their feedback and testimonials.
Last week and throughout the weekend, Deflect helped mitigate several DDoS attack bursts against the official Black Lives Matter website. At current estimates over 12,000 bots pounded the website just over 35 million times in 24 hours. An unusual trait of this attack was the prevalence of malicious connections originating from the US. An in-depth analytic report will follow this prima facie bulletin.
Despite its intensity, the attack has been successfully contained by Deflect, and the Black Lives Matter website is functional and accessible throughout much of the weekend. Black Lives Matter has released an official statement on this incident together with eQualit.ie, Design Action Collective and May First/People Link:
Keeping a website available when attackers are seeking to take it off-line is essential for many reasons. The most obvious is the importance of protecting the fundamental right to human communication. But the specific targeting that characterizes recent DDOS attacks (on networks supporting reproductive rights, Palestinian rights and the rights of people of color) highlights this type of on-line attack as part of the arsenal being used to quash response and social change movements.
DDOS attacks will increase as our protests and organizing increases and so must our movements’ ability to resist them and stay on-line. The collaborative work that spawned the response to this attack is both an example of this protective effort and yet another step in improving it and making it stronger.
Our organizations work in different areas with different programs but we are united in our commitment to vigorously preserving our movements’ right to communicate and defeating all attempts to curtail that right. Without the ability to communicate freely, we can’t organize and, if we can’t organize, our world can never be truly free.
We are in the process of studying and classifying these attack using Deflect Labs technology and aim to publish the results in our next Deflect Labs report.
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.
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 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.
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Graph 2. Total hits to the website, by country of origin. The spikes represent attacks investigated in this report
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Graph 3. Incidents where the WordPress pingback attack is used against the target site
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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.
[one_half] 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%
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Graph 4. Hits from bots and their country of origin, grouped by identified botnets. Update your software and malware cleaners please!
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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.
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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
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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.
UA: low variation (slightly higher than botnet 1), most major UAs represented
Deflect Response: Moderate blocking success, origin was affected.[/one_half_last]
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The botnet utilises several hundred unique IPs and a few dozen rotating user agents
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The botnet attacks with several hundred unique IPs and rotates through a few dozen user agents. Graph tallies at 15 second intervals.
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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.
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Graph 6. Hits against target website, by their geographic origin.
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Graph 7. The same timespan as per the previous graph, only this time showing a count of unique IPs, per country geoIP
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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.
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).
[one_third] 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
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
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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.[one_half]
Graph 9. Hit count from various devices throughout incidents 31-32
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Graph 10. Unique IP count from various devices throughout incidents 33-34
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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]
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 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
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.
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 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)
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 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 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: “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
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.
