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BlackWidow: Monitoring
the Dark Web for Cyber
Security Information
Abstract: The Dark Web, a conglomerate of services hidden from search engines
and regular users, is used by cyber criminals to offer all kinds of illegal services and
goods. Multiple Dark Web offerings are highly relevant for the cyber security domain
in anticipating and preventing attacks, such as information about zero-day exploits,
stolen datasets with login information, or botnets available for hire.
In this work, we analyze and discuss the challenges related to information gathering
in the Dark Web for cyber security intelligence purposes. To facilitate information
collection and the analysis of large amounts of unstructured data, we present
BlackWidow, a highly automated modular system that monitors Dark Web services
and fuses the collected data in a single analytics framework. BlackWidow relies on a
Docker-based micro service architecture which permits the combination of both pre-
existing and customized machine learning tools. BlackWidow represents all extracted
Matthias Schäfer
Department of Computer Science
University of Kaiserslautern
Kaiserslautern, Germany
schaefer@cs.uni-kl.de
Martin Strohmeier
Cyber-Defence Campus
armasuisse
Thun, Switzerland
martin.strohmeier@armasuisse.ch
Marc Liechti
Trivo Systems
Bern, Switzerland
marc.liechti@trivo.ch
Markus Fuchs
SeRo Systems
Kaiserslautern, Germany
fuchs@sero-systems.de
Markus Engel
SeRo Systems
Kaiserslautern, Germany
engel@sero-systems.de
Vincent Lenders
Cyber-Defence Campus
armasuisse
Thun, Switzerland
vincent.lenders@armasuisse.ch
2019 11th International Conference on Cyber Conict:
Silent Battle
T. Minárik, S. Alatalu, S. Biondi,
M. Signoretti, I. Tolga, G. Visky (Eds.)
2019 © NATO CCD COE Publications, Tallinn
Permission to make digital or hard copies of this publication for internal
use within NATO and for personal or educational use when for non-prot or
non-commercial purposes is granted providing that copies bear this notice
and a full citation on the rst page. Any other reproduction or transmission
requires prior written permission by NATO CCD COE.
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1. INTRODUCTION
The Dark Web is a conglomerate of services hidden from search engines and regular
Internet users. Anecdotally, it seems to the uneducated observer that anything that is
illegal to sell (or discuss) is widely available in this corner of the Internet. Several
studies have shown that its main content ranges from illegal pornography to drugs and
weapons [1], [2]. Further work has revealed that there are many Dark Web offerings
which are highly relevant for the cyber security domain. Sensitive information about
zero-day exploits, stolen datasets with login information, or botnets available for hire
[2], [3] can be used to anticipate, discover, or ideally prevent attacks on a wide range
of targets.
It is difcult to truly measure the size and activity of the Dark Web, as many websites
are under pressure from law enforcement, service providers, or their competitors.
Despite this, several web intelligence services have attempted to map the reachable
part of the Dark Web in recent studies. One crawled the home pages of more than
6,600 sites (before any possible login requirement), nding clusters of Bitcoin
scams and bank card fraud [4]. Another study found that more than 87% of the sites
measured did not link to other sites [5]. This is very different from the open Internet,
both conceptually and in spirit: in contrast, we can view the Dark Web as a collection
of individual sites or separated islands.
In the present work, we introduce BlackWidow, a technical framework that is able to
automatically nd information that is useful for cyber intelligence, such as the early
data and the corresponding relationships extracted from posts in a large knowledge
graph, which is made available to its security analyst users for search and interactive
visual exploration.
Using BlackWidow, we conduct a study of seven popular services on the Deep and
Dark Web across three different languages with almost 100,000 users. Within less
than two days of monitoring time, BlackWidow managed to collect years of relevant
information in the areas of cyber security and fraud monitoring. We show that
BlackWidow can infer relationships between authors and forums and detect trends for
cybersecurity-related topics. Finally, we discuss exemplary case studies surrounding
leaked data and preparation for malicious activity.
Keywords: Dark Web analysis, open source intelligence, cyber intelligence
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detection of exploits used in the wild, or leaked information. Naturally, analyzing a
part of the Internet frequented by individuals who are trying to stay out of the spotlight
is a more difcult task than traditional measurement campaigns conducted on the
Surface Web.
Thus, a system that seeks to present meaningful information on the Dark Web needs
to overcome several technical challenges – a large amount of unstructured and
inaccessible data needs to be processed in a scalable way that enables humans to collect
useful intelligence quickly and reliably. These challenges range from scalability and
efcient use of resources over the acquisition of tting targets to the processing of
different languages, a key capability in a globalized underground marketplace.
Yet, contrary to what is sometimes implied in media reports, few underground forums
and marketplaces use a sophisticated trust system to control access outright, although
some protect certain parts of their forums, requiring a certain reputation [6]. We
successfully exploit this fact to develop an automated system that can gather and
process data from these forums and make them available to human users.
In this work, we make the following contributions:
• We present and describe the architecture of BlackWidow, a highly
automated modular system that monitors Dark Web services in a real-time
and continuous fashion and fuses the collected data in a single analytics
framework.
• We overcome challenges of information extraction in a globalized world
of cyber crime. Using machine translation techniques, BlackWidow can
investigate relationships between forums and users across language barriers.
We show that there is signicant overlap across forums, even across different
languages.
• We illustrate the power of real-time intelligence extraction by conducting
a study on seven forums on the Dark Web and the open Internet. In this
study, we show that BlackWidow is able to extract threads, authors and
content from Dark Web forums and process them further in order to create
intelligence relevant to the cyber security domain.
The remainder of this work is organized as follows. Section 2 provides the background
on the concepts used throughout, while Section 3 discusses the challenges faced
during the creation of BlackWidow. Section 4 describes BlackWidow’s architecture
before Sections 5 and 6 respectively present the design and the results of a Dark Web
measurement campaign. Section 7 discusses some case studies, Section 8 examines
the related work and nally Section 9 concludes this paper.
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2. BACKGROUND
In this section, we introduce the necessary background for understanding the
BlackWidow concept. In particular, we provide the denitions and also explain the
underlying technological concepts relating to the so-called Dark Web and to Tor
Hidden Services.
A. The Deep Web and Dark Web
The media and academic literature are full of discussions about two concepts, the
Dark Web and the Deep Web. As there are no clear ofcial technical denitions, the
use of these terms can easily become blurred. Consequently, these terms are often used
interchangeably and at various levels of hysterics. We provide the most commonly
accepted denitions, which can also be used to distinguish both concepts.
1) The Deep Web
The term ‘Deep Web’ is used in this work to describe any type of content on the
Internet that, for various deliberate or non-deliberate technical reasons, is not indexed
by search engines. This is often contrasted with the ‘Surface Web’, which is easily
found and thus accessible via common search engine providers.
Deep Web content may, for example, be password-protected behind logins; encrypted;
its indexing might be disallowed by the owner; or it may simply not be hyperlinked
anywhere else. Naturally, much of this content could be considered underground
activity, e.g., several of the hacker forums that we came across for this work were also
accessible without special anonymizing means.
However, the Deep Web also comprises many sites and servers that serve more noble
enterprises and information, ranging, for example, from government web pages
through traditional non-open academic papers to databases where the owner might
not even realize that they are accessible over the Internet. By denition, private social
media proles on Facebook or Twitter would be considered part of the Deep Web, too.
2) The Dark Web
In contrast, the Dark Web is a subset of the Deep Web which cannot be accessed using
standard web browsers, but instead requires the use of special software providing
access to anonymity networks. Thus, deliberate steps need to be taken to access the
Dark Web, which operates strictly anonymously both for the user and the service
provider (e.g., underground forums).
There are several services enabling de facto access to anonymity networks, for
example the Invisible Internet Project (IIP) or JonDonym [7]. However, the so-called
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‘Hidden Services’ provided by the Tor project remain the most popular de facto
manifestation of the Dark Web. In the next section we provide a detailed technical
explanation of Tor’s Hidden Service feature, which formed the basis of the analysis
done by BlackWidow.
B. Tor Hidden Services
Tor, originally short for The Onion Router, is a project that seeks to enable low-latency
anonymous communication through an encrypted network of relays. Applying the
concepts of onion routing and telescoping, users obtain anonymity by sending their
communication through a so-called Circuit of at least three relay nodes.
As Tor is effectively a crowdsourced network, these relays are largely run by
volunteers. The network has been an important tool for many Internet users who
depend on anonymity, from dissidents to citizens in countries with restricted Internet
access. However, there have been many vulnerabilities found and discussed in the
literature which could lead to deanonymization of Tor users. As it is not desired to
authenticate the identity of every Tor relay, it is widely considered possible that state
actors such as intelligence agencies run their own relay nodes, by which they may
exploit some of these vulnerabilities in order to deanonymize users of interest [8].
Despite these potential threats, Tor is the best-known and most popular way to hide
one’s identity on the Internet.
Besides enabling users to connect to websites anonymously, Tor offers a feature called
Hidden Services. Introduced in 2004, it adds anonymity not only to the client but also
to the server, also known as responder anonymity. More concretely, by using such
Hidden Services, the operator of any Internet service (such as an ordinary web page,
including forums or message boards, which we are interested in for this work) can
hide their IP address from the clients perusing the service. When a client connects
to the Hidden Service, all data is routed through a so-called Rendezvous Point. This
point connects the separate anonymous Tor circuits from both the client and the true
server [9].
Figure 1 illustrates the concept: overall, there are ve main components that are part
of a Hidden Service connection. Besides the Hidden Service itself, the client and the
Rendezvous Point, it requires an Introduction Point and a Directory Server.
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FIGURE 1. GENERAL ILLUSTRATION OF THE TOR HIDDEN SERVICE CONCEPT.
The former are Tor relays, which forward management information necessary to
establish the connection via the Rendezvous point and are selected by the Hidden
Service itself, which is necessary to connect the client and the Hidden Service at the
Rendezvous point. The latter are Tor relay nodes, where Hidden Services publish
their information and which are then communicated to clients in order to learn the
addresses of the Hidden Service’s introduction points. These directories are often
published in static lists and are in principle used to nd the addresses for the web
forums used in BlackWidow.
It is unsurprising that Tor Hidden Services are a very attractive concept for all sorts
of underground websites, such the infamous Silk Road or AlphaBay and due to their
popularity form in effect the underlying architecture of the Dark Web.
3. CHALLENGES IN DARK WEB MONITORING
The overarching main issues in analyzing the Dark Web for cyber security intelligence
relate to the fact that a vast amount of unstructured and inaccessible information
needs rst to be found and then processed. This processing also needs to be done in a
scalable way that enables humans to collect useful intelligence quickly and reliably.
In the following, we outline the concrete challenges that needed to be overcome in
developing BlackWidow.
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A. Acquisition of Relevant Target Forums
The rst challenge is the identication of target forums that are relevant to our operation,
i.e. those that contain users and content relating to cyber security intelligence. Due to
the underground nature of the intended targets, there is no curated list available that
could be used as input to BlackWidow. Intelliagg, a cyber threat intelligence company,
recently attempted to map the Dark Web by crawling reachable sites over Tor. They
found almost 30,000 websites; however, over half of them disappeared during the
course of their research [1], illustrating the difculty of keeping the information about
target forums up to date.
Combined with the mentioned previously fact that 87% of Dark Web sites do not
link to any other sites, we can deduce that the Dark Web is more a set of isolated
short-lived silos than the classical Web, which has a clear and stable graph structure.
Instead, only loose and often outdated collections of URLs (both from the surface
Internet as well as Hidden Services) exist on the Dark Web. Consequently, a fully
automated approach to overcome this issue is infeasible and a semi-manual approach
must initially be employed.
B. Resource Requirements and Scalability
Several technical characteristics of the acquired target forums require the use of more
signicant resource inputs. As is typical in analyzing large datasets obtained from the
Dark Web, it is necessary to manage techniques which limit the speed and the method
of access to the relevant data [10].
Such techniques include the deliberate (e.g., articial limiting of the number of requests
to a web page) and the non-deliberate (e.g., using active web technologies such as
NodeJS, which break the use of faster conventional data collection tools). Typically,
these issues can be mitigated by expending additional resources. Using additional
virtual machines, bandwidth, memory, virtual connections or computational power,
we can improve the trade-off with the time required for efcient data collection. For
example, by using several virtual private networks (VPNs) or Tor circuits, it is possible
to parallelize the data collection in case there is a rate limit employed by the target.
Surprisingly, a factor not challenging our resources was the habit of extensively
vetting the credentials or ‘bona des’ of forum participants before allowing access.
A sufcient number of the largest online forums are available without this practice,
which enabled data collection and analysis without having to manually circumvent
such protection measures. However, since we did encounter at least some such forums
(or parts of forums), our approach could naturally be extended to them, although this
would require signicant manual resource investment.
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C. Globalized Environment
As cyber security and cyber crime have long become a global issue, underground
forums with relevant pieces of information are available in practically all languages
with a signicant number of speakers. Most existing studies of Dark Web content
have focused on English or another single language (e.g., [2]). However, the ability
to gather and combine information independent of the forum language broadens the
scope and the scale of BlackWidow signicantly. By employing automated machine
translation services, we are able to not only increase the range of our analysis but
also detect relationships and common threads and topics across linguistic barriers and
country borders.
Naturally, this approach comes with several downsides. For example, it is not possible
to employ sentiment or linguistic analysis on the translated texts nor is the quality of
state-of-the-art machine translation comparable to the level of a human native speaker.
However, given BlackWidow’s aims of scalable and automatic intelligence gathering,
these disadvantages can be considered an acceptable trade-off.
D. Real-Time Intelligence Extraction
Beyond the previous issues, BlackWidow focuses in particular on the challenges
posed by the nature of a real-time intelligence extraction process. Whereas previous
studies have collected data from the Dark Web for analytical purposes, they have
typically concentrated on a static environment. In contrast to collecting one or several
snapshots of the target environment, BlackWidow aims to provide intelligence and
insights much faster. Real-time capability is a core requirement for the longer-term
utility of the system, due to the often very limited lifetime of the target forums.
To enable these functionalities, a high grade of automation is required, from the
collection to the live analysis of the data. After the initial bootstrapping of sources and
creating a working prototype, it is imperative that the processes require less manual
input beyond normal human oversight tasks.
4. ARCHITECTURE OF BLACKWIDOW
In this section, we describe the basic architecture of BlackWidow. We largely abstract
away from the exact technologies used and focus on the processing chain and the data
model that enabled us to analyze the target forums in real time. Figure 2 shows the
processing chain, including ve phases dened as a recurrent cycle. The phases of the
cycle are highly inspired by the conceptual model of the intelligence cycle [11]. Like
the intelligence cycle, theses phases are continuously iterated to produce new insights.
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FIGURE 2. BLACKWIDOW PROCESS CYCLE.
A. Planning and Requirements
The key focus of BlackWidow is on automation; manual work should only be needed
for the integration of target forums in the initial planning and requirements phase,
while all other phases are highly automated.
1) Identifying Dark Web Forums
The rst suitable target forums are identied by hand to bootstrap the process and
overcome the challenges described in Section 3.A. After obtaining a foothold,
BlackWidow then aims to analyze the content of these forums in order to obtain further
links and addresses to other targets in a more automated fashion in later iterations.
2) Gaining access
Since most forums require some sort of login to access the site, BlackWidow needs
personal accounts to authenticate on each site. The way to acquire such logins differs
on each site. While certain sites only request new users to provide a valid email
address, others have higher entry barriers with reputation systems, measures of active
participation, or even requiring users to rst buy credits.
B. Collection
After the planning and requirements phase, all steps are fully automated. The
collection phase deals with establishing anonymous access to the forums over Tor and
the collection of raw data.
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1) Establishing anonymous access to forums
We establish anonymous gateways to the identied forums using Docker containers,
Tor to access Hidden Services and Virtual Private Networks (VPN) for regular Deep
Web sites. Here, it is necessary to add custom functions to BlackWidow, which emulate
typing and clicking behavior in order to log in automatically and subsequently detect
whether the gateway has successfully logged into the target or not.
2) Collection of raw data
For the actual collection of the forum content and metadata, we employed the node.js
headless Chrome browser puppeteer [12] as a crawler within the Docker containers.
While it requires more resources than other collection methods, it more closely
emulates the behavior of real forum users, meaning it more easily avoids defensive
action by the Dark Web marketplace operators. In order to improve the speed, the
collection is distributed across multiple containers and parallelized.
C. Processing
The processing phase deals with parsing the collected raw HTML data from the
previous phase, translating the content into English and extracting the entities of
interest to feed a knowledge graph.
1) Parsing raw HTML data
Since BlackWidow retrieves data over a headless browser, the data to process is in the
Hypertext Markup Language (HTML). Extracting structured information from HTML
data can be quite challenging depending on the layout of the forums. BlackWidow
implements a standard HTML parser that we adapt to the layout of each forum. While
this approach may seem expensive at rst, many forums have a similar layout such
that the same parsers can be reused for different forums. The output of the HTML
parser for each page is a structured le including only the text information from the
HTML page.
2) Translation of raw data in foreign languages
As much of the collected raw data contains content in several languages, we used
automated machine translation to convert all non-English content into English.
Through the use of Google’s translation API, we obtain state-of-the-art translations,
which enable the more complex data modeling and relationship analysis over forums
in different languages in the follow-up phases.
3) Information extraction
To extract relevant information from the translated text from the gateways, we
developed so-called extractors in Scala, which were also processed in a distributed
fashion using the Apache Spark analytics framework. BlackWidow extracts
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information about the forum writers and their content, i.e. the titles of forum threads
and the posted messages. It then constructs a knowledge graph that connects threads,
actors, messages and topics. Figure 3 shows the underlying data model of the
knowledge graph of BlackWidow. The collected raw data and the knowledge graph is
then put into Elasticsearch, a search engine based on Lucene [13]. As a tool for data
exploration, it reads structured data and interprets timestamps and locations.
FIGURE 3. DATA MODEL REPRESENTING THE KNOWLEDGE GRAPH IN BLACKWIDOW.
D. Analysis
While inferring simple relationships between messages and authors is a relatively
easy task given the HTML structure of the forums, other types of relationships and
information extraction steps for the knowledge graph require advanced data analysis
techniques. BlackWidow’s goal is to automatically nd relationships and trends
across different threads and forums; the following processing steps are thus executed
in this phase.
1) Infer user relationships
Relationships between users are mainly inferred in BlackWidow through the analysis
of threads, since users of Dark Web forums barely link to each other explicitly as in
classical social networks such as Facebook or LinkedIn [6]. A thread is always created
by a single user and many different users then start posting messages on this thread.
BlackWidow infers a relationship between users by ordering all messages in the same
thread by their message times. We dene a relationship from user A to user B if user
B posted a message after user A in the same thread. The intuition is that user B is
interacting with user A when he replies to his messages.
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2) Identify topics
While messages in forums are commonly structured in threads and categories, it is
not always obvious to see which threads cover the same topics. To facilitate trend
analysis across different threads and forums, BlackWidow automatically identies
topics by means of automatic topic modeling. BlackWidow implements unsupervised
text clustering techniques based on Latent Dirichlet Allocation (LDA) to classify
messages into groups. These groups are then assigned to higher-level categories of
interests such as botnets, databases, exploits, leaks and DDoS.
3) Identify cyber security trends
To identify cyber security trends, BlackWidow fuses the messages, topics and
categories from the different forums and computes aggregated time series. These time
series form the basis to identify trending topics, e.g. when the time series experiences
a high growth or decline over short periods. Long-term trends are also detectable
given that all collected messages are time-stamped and thus provide information over
the whole lifetime of the forum.
E. Dissemination
Finally, it is important to disseminate the extracted information so that it can be
easily processed by human intelligence analysts. To serve this purpose, BlackWidow
supports various types of data visualizations and data query interfaces for exploratory
analysis. For example, customized Kibana dashboards provide real-time views of the
processed data that is stored in the Elasticsearch database. These dashboards can be
generated and customized easily by the users allowing different views depending on
the question of interest.
Finally, users may realize that some data is missing or that the additional forums
should be integrated. The cycle of BlackWidow’s architecture supports users to rene
the planning and data collection requirements, thus closing the loop of the intelligence
process.
5. STUDY DESIGN
After describing the architecture of BlackWidow, we now explain the goals of the
study conducted for this paper. The study was designed to show the power and
effectiveness of our automated data extraction and analysis efforts for the Dark Web.
A. Information Extraction
Forum contents are usually structured hierarchically. Users provide or exchange
information by posting messages, known as “posts”. Collections of posts belonging to
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the same conversation are called threads. Threads can be separated by categories such
as “Drugs”, “Exploits”, or “Announcements”. Besides the actual message, posts also
provide meta information on the author (e.g., username, date of registration) and the
exact date and time when the message was posted.
While posts are certainly the most interesting source of information in a forum, it is
worth taking other parts of the forum into account for information retrieval as well. For
example, most forums have a publicly available list of members which provides links
to the proles of all users registered in the forum. By additionally crawling the public
proles of all registered users, it is possible to gather information on passive users and
the overall community as well. User proles often provide useful information, such as
registration date and time of last visit.
To extract all this information from the HTML-based forum data collected by
BlackWidow, we implemented HTML parsers for each forum based on jsoup.
Although forums generally have a very similar structure, the underlying HTML
representations differ signicantly depending on the platform. The consequence is
that for each different forum platform (e.g., vBulletin), a separate forum parser is
required.
For this analysis, we limit our implementation to parsing posts and user proles. Our
parsers transform the HTML-based representation of posts and user proles into a
unied JSON-based format. More specically, each post is transformed into a JSON
object with attributes forum, category, thread, username, timestamp and message.
Objects from non-English forums are extended with the English translations of
categories, threads and messages. User proles are parsed into JSON objects with
attributes forum, username, registration date and (where available) last visit date.
B. Forum Selection
For the purpose of this study, we collected data from seven forums as a proof of
concept, as the manual integration of new forums can require signicant time
investment. At the time of writing, roughly one year after collecting the data, only
four of the scanned forums are still online, conrming the short lifetime and high
volatility of such forums. Overall, three of the seven forums were only accessible in
the Dark Web and four were Deep Web forums. The languages used in the forums
were Russian, English and French. An overview over the considered forums and the
most popular categories (by number of posts) is provided in Table 1.
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TABLE 1: OVERVIEW OF THE FORUMS CONSIDERED IN OUR ANALYSIS.
6. STUDY RESULTS
A. Target Analysis
The size of each forum can be determined either in the number of posts or in the
number of users. Both metrics for the crawled forums are shown in Figure 4 and 5.
Forum 5 has by far the largest community with 67,535 registered users, while Forum 3
has (also by a considerable margin) the most content with over 288,000 posts. Forum
3 is also the forum with the most active community in terms of average posts per
user. On average, each user had posted 22.74 messages in Forum 3. In contrast, the
community of Forum 5 seemed to consist largely of passive users, since for each user,
there were only 2.28 messages, roughly one tenth of those in Forum 3.
FIGURE 4. NUMBER OF USERS EXTRACTED FROM EACH FORUM.
#
Forum 1
Forum 2
Forum 3
Forum 4
Forum 5
Forum 6
Forum 7
Type
Deep Web
Deep Web
Dark Web
Dark Web
Deep Web
Dark Web
Deep Web
Online
as of 12/
2018
Yes
No
No
Yes
Yes
No
Yes
Language
English
Russian
French
Russian
English
French
Russian
Top Categories
News, Porn, Software, Drugs
Marketplace, Electronic Money, Hacking
Drugs, News, Porn, Technology
Marketplace, General Discussions, Hacking, Security
Gaming, Leaks, Cracking, Hacking, Monetizing Techniques, Tutorials
News, Frauds, Conspiracy Theories, Drugs, Crime
Software, Security & Hacking, DDoS Services, Marketplace
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FIGURE 5. NUMBER OF POSTS EXTRACTED FROM EACH FORUM.
We hypothesize that the extremely large number of passive users in Forum 5 comes
from the fact that the forum is a Deep Web forum, meaning that it does not require
users to use additional software (such as the Tor browser) to sign up. As a consequence
of this signicantly lower technical hurdle, is can be accessed much more easily than
Dark Web forums and is therefore open to a broader, less tech-savvy audience.
B. Forum Relationships
In order to get some insights on the relationships between the forums, we compared
the sets of usernames of the forums. More specically, we were interested in the
intersections of these sets to see whether these forums host separate communities or
whether there are signicant overlaps. Surprisingly, those usernames that appeared
most often were very specic, suggesting that they actually belonged to the same
person. In fact, generic usernames such as “admin” or “john” were very rarely
seen. Instead, users tended to individualize their usernames, for example by using
leetspeek,1 most likely as a means of anonymous branding. This tendency benets
the social network analysis conducted in this section since it provides us with reliable
information about individual users, even across forums.
The result of this analysis is depicted as a chord diagram in Figure 6. Unsurprisingly,
there are signicant overlaps across forums in the same language. More interesting,
however, is the fact that Forum 5, the forum with the largest community, has signicant
overlaps with most other forums, even if they are in a different language. By looking at
these intersections as information dissemination channels, Forum 5 certainly provides
the best entry point to spread information across the deep and dark side of the web.
1 A system of modied spelling, whereby users replace characters with resembling glyphs.
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FIGURE 6. RELATIONSHIPS BETWEEN THE FORUMS IN TERMS OF COMMON USERS.
C. Author Relationships
In order to analyze the internal relationships between users of forums, we rst need
to establish a reasonable denition of user relationships. While there are clearly
dened relationships in social networks such as Facebook or Twitter, forum users
do not have natural links such as friendships or followers. Given the hierarchical
structure of forums, however, we can identify users with common interests by looking
at the threads in which they are active together. We therefore dene the relationships
between two users in a forum by the number of threads in which both users posted
messages.
Based on the creation timestamp of each post, we can also add a direction to this
relationship by acknowledging which user merely reacted to a post of another user;
i.e., which user posted a message in the same thread at a later point in time. This
directed relationship will help us distinguish information or service providers from
consumers. This is possible since a common communication pattern, for example in
forum-based marketplaces, is that someone shares data or services in a new thread
and interested users must post a reply (e.g., “thank you”) in order to access the shared
content.
After these relationships were established, we used the Walktrap community-nding
algorithm [14] with a length of 4 to determine sub-communities in the forums. These
sub-communities evolve naturally since forums often cover many different unrelated
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topics. For example, users interested in drugs might not be interested in hacking and
vice versa, resulting in two sub-communities.
FIGURE 7. NETWORK SHOWING THE RELATIONSHIPS
BETWEEN USERS OF FORUM 4 (LEFT) AND 5 (RIGHT).
The result of this analysis for Forums 4 and 5 is shown in Figure 7. The vertices
in the graphs represent the individual users, while the (directed) edges show the
relationships as dened above. Each sub-community is indicated by a color. The
size of each node in the network represents the number of incoming edges, i.e.,
its degree. In comparison, the structural differences of the communities of the two
forums are clearly visible. Forum 4, which has a much smaller community, is much
denser, meaning that there are many more relationships between users, even across
the different sub-communities.
The network analysis enabled us to select sub-communities and identify their key users,
i.e., the most active information or service providers. For instance, the completely
separate sub-community in Forum 5 is a group of so-called skin gamblers, i.e., people
who bet virtual goods (e.g., cryptocurrencies) on the outcome of matches or other
games of chance. Another sub-community in Forum 5 deals with serial numbers of
commercial products, with one user being a particularly active provider.
It is worth noting that, besides active providers, forum administrators and moderators
also stick out in terms of node degree (activity) as they post a lot of administrative
messages. For example, one user was very prominent in a sub-community and by
manually checking his posts we found that he was a very active moderator who
enforced forum rules very strictly and made sure transactions were being handled
correctly. His power to enforce rules and certain behavior was established by a system
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of reputation, in which users must gain hard-earned reputation points, for example
by posting free content or being an active community member over a long period of
time. Once a certain reputation is earned by a user, it becomes much easier for her or
him to sell products on the marketplace; or they can charge higher prices as the risk
of scam for buyers is lower. This system provides administrators and moderators with
a certain leverage, since a ban from the forum would mean a complete loss of hard-
earned reputation.
7. CYBER SECURITY INTELLIGENCE EXAMPLES
After conducting our quantitative study, we now discuss some exemplary trends and
case studies that we noticed using BlackWidow during its initial deployment in 2017
to collect and analyze forum datasets dating back to 2012.
A. Forum Trends
FIGURE 8. CYBERSECURITY TRENDS BETWEEN 2012 AND 2017
IN SEVEN FORUMS AS OBSERVED BY BLACKWIDOW.
It is possible to use BlackWidow’s functionality to look at the popularity of different
concepts over time, which can aid the intelligence analyst in nding sudden anomalies
or identifying trends that suggest increased or decreased importance. Figure 8 shows
the fraction of all posts for the ve most popular identied cyber security categories
over a time frame of ve years from the end of 2012 to the end of 2017. The time
series are generated using the running mean of the number of posts in the respective
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topics over time and normalized with respect to the overall activity in the considered
period. The topic assignment is based on regular expressions and string matching.
From this, we can see a substantial change in the number of times that forum actors
were discussing leaks, which increases roughly ten-fold in 2017 and outpaces the
other groups in number of mentions signicantly by the end of the period. Related to
leaks, posts on databases seem to become increasingly popular, while talk of exploits
remains more or less constant as a trend, with several peaks, e.g. at the end of 2014.
DDoS and botnets are the least popular of the ve; the signicant DDoS peak in the
beginning of 2016 was caused by one of the analyzed forums itself being the victim
of a DoS attack.
B. Discovered Leaks and Exploits
During the course of our study, we encountered various data leaks consisting of
usernames and passwords. As an example, BlackWidow crawled links to a list of
half a million leaked Yahoo! accounts, a well-known dataset from a hack in 2014
(ofcially reported by Yahoo! in 2016). Perhaps surprisingly, these leaked datasets
were accessible for free through direct links, highlighting again that security-relevant
information can indeed be automatically collected by BlackWidow without interacting
personally with criminal data brokers.
Exploits for various platforms were also found abundantly. Again, the open nature
of the forums makes it possible to collect large amounts of exploits for free. While a
systematic analysis on the quality and novelty of the individual exploits is outside the
scope of this paper, we are condent that BlackWidow constitutes a very useful data
source to better understand the cyber threat landscape and anticipate exploits that may
be expected in the wild. Security professionals and defenders should therefore aim at
analyzing such information to anticipate emerging threats.
8. RELATED WORK
Web forums inside and outside the Dark Web have been an active eld of research
in the recent past, with authors approaching them from a wide variety of angles,
including cyber security and intelligence.
The closest works to ours relate to underground crawling systems. Pastrana et al. [6]
recently built a system that looks at cyber crime outside the Dark Web. The authors
discuss challenges in crawling underground forums and analyze four English-speaking
communities on the Surface Web. In contrast, Nunes et al. [15] mine Dark Web and
Deep Web forums and marketplaces for cyber threat intelligence. They show that it is
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possible to detect zero-day exploits, map user/vendor relationships and conduct topic
classication on English-language forums, results that we have been able to reproduce
with BlackWidow.
Benjamin et al. [16] explore cyber security threats in what they call the “hacker web”,
with a focus on stolen credit card data activity but also potential attack vectors and
software vulnerabilities. The authors extract data from carding shops, the Internet
Relay Chat (IRC) and web forums, but do not investigate Tor Hidden Services.
In [17] and [18], the authors look at major hacker communities in the US and China,
aiming to identify key players, experts and relationships in open web forums. They
base their approach on a framework for automated extraction of features using text
analytics and interaction coherence analysis. Similarly, Motoyama et al. [19] look
at six different underground forums on the open web, providing a measurement
campaign on historical data. The extensive quantitative data analysis covers features
from the top content over the size of the overlapping user base to interactions and
relationships between the users. However, their analysis is based on leaked SQL
dumps of the forums, while BlackWidow is a framework that collects information in
real time through the frontend of the forums.
Outside the academic literature, we nd several commercial enterprises which aim to
conduct automated analysis of cyber security intelligence from the Dark Web, among
other sources. Two examples are provided by DarkOwl [20] and Recorded Future
[21], which monitor the Dark Web in several languages and offer to detect threats,
breached data and indicators of compromise.
To the best of our knowledge, this paper is the rst to discuss real-time data collection
in the Deep and Dark Web and the integration of external translation capabilities in a
scalable way. Additionally, our results have been able to show that there is substantial
overlap between actors across forums, even if they are not in the same language.
9. CONCLUSION
It is imperative in the current cyber security environment to have a real-time monitoring
solution that works across languages and other barriers. We have shown in this paper
that early detection of cyber threats and trends is feasible by overcoming several key
challenges towards a comprehensive framework.
While we can be fairly certain that techniques similar to ours are being used by both
governmental and private intelligence actors around the world, it is important to
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analyze their power in a more open fashion, giving rise to possible scrutiny and further
development. By implementing BlackWidow as a proof-of-concept collection and
analysis tool, we show that monitoring of the Dark Web can be done with relatively
little resources and time investment, making it accessible to a broader range of actors
in the future.
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