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Key Player Identification in Underground Forums Over Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework Yiming Zhang, Yujie Fan, Liang Zhao Chuan Shi Yanfang Ye∗ Department of IST School of CS Department of CDS, Case Western George Mason University Beijing University of Posts and Reserve University, OH, USA VA, USA Telecommunications, Beijing, China ABSTRACT ACM Reference Format: Online underground forums have been widely used by cybercrimi- Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, and Chuan Shi. 2019. Key Player Identification in Underground Forums over Attributed Hetero- nals to exchange knowledge and trade in illicit products or services, geneous Information Network Embedding Framework. In The 28th ACM which have played a central role in the cybercriminal ecosystem. In International Conference on Information and Knowledge Management (CIKM order to combat the evolving cybercrimes, in this paper, we propose ’19), November 3–7, 2019, Beijing, China. ACM, New York, NY, USA, 10 pages. and develop an intelligent system named iDetective to automate https://doi.org/10.1145/3357384.3357876 the analysis of underground forums for the identification of key players (i.e., users who play the vital role in the value chain). In 1 INTRODUCTION iDetective, we first introduce an attributed heterogeneous informa- tion network (AHIN) for user representation and use a meta-path As the Internet has become one of the most important drivers in the based approach to incorporate higher-level semantics to build up global economy (e.g., worldwide e-commerce sales reached over relatedness over users in underground forums; then we propose $2.3 trillion dollars in 2017 and its revenues are projected to grow Player2Vec to efficiently learn node (i.e., user) representations in to $4.88 trillion dollars in 2021 [31]), it also provides an open and AHIN for key player identification. In Player2Vec, we first map shared platform by dissolving the barriers so that everyone has the constructed AHIN to a multi-view network which consists of opportunity to realize his/her innovations, which implies higher multiple single-view attributed graphs encoding the relatedness prospects for illicit profits at lower degrees of risk. That is,the over users depicted by different designed meta-paths; then we em- Internet can virtually provide a natural and excellent platform for ploy graph convolutional network (GCN) to learn embeddings of illegal Internet-based activities, commonly known as cybercrimes each single-view attributed graph; later, an attention mechanism is (e.g., hacking, online scam, credit card fraud). Cybercrimes have designed to fuse different embeddings learned based on different become increasingly dependent on the online underground forums, single-view attributed graphs for final representations. Comprehen- through which cybercriminals can exchange knowledge (including sive experiments on the data collections from different underground ideas, methods and tools) and trade in illicit products (e.g., malware, forums (i.e., Hack Forums, Nulled) are conducted to validate the ef- stolen credit cards) or services (e.g., hacking services, bogus Ama- fectiveness of iDetective in key player identification by comparisons zon reviews). The emerging underground forums, such as Hack with alternative approaches. Forums [14], Nulled [28], and Black Hat World [4], have enabled cybercriminals to realize considerable profits. For example, the CCS CONCEPTS estimated annual revenue for an individual credit card steal orga- nization was $300 millions [26]; it’s also revealed that a group of • Security and privacy → Web application security; • Net- cybercriminals could profit $864 millions per year by renting out works → Online social networks; • Computing methodologies the DDoS attacks [20]. → Machine learning algorithms. Underground forums, which provide the platforms for cybercrim- inals to exchange knowledge and trade in illicit products or services KEYWORDS that facilitate all stages of cybercrimes, have played a central role in Underground forums; key player identification; attributed hetero- the cybercriminal ecosystem. As one of the most prevalent under- geneous information network; network embedding ground forums, Hack Forums consists of 640,820 registered users with 3,621,989 posted threads containing 790,286 illicit products or ∗ Corresponding author: [email protected] services. We here use Hack Forums as a showcase to investigate the profit model and monetization process. As shown in Figure Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed 1, the participants in the value chain can be categorized into dif- for profit or commercial advantage and that copies bear this notice and the full citation ferent groups according to the roles they play: (1) Key players: on the first page. Copyrights for components of this work owned by others than ACM This group of users play a vital role in the value chain, as they are must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a the “decathlon” who are capable of exploiting and disseminating fee. Request permissions from [email protected]. vulnerabilities, developing and testing malicious tools, selling and CIKM ’19, November 3–7, 2019, Beijing, China monetizing illicit products or services. For example, as shown in © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-6976-3/19/11...$15.00 Figure 1, we found that a Hack Forums user “Ban***s” (we here https://doi.org/10.1145/3357384.3357876 anonymize his user name) first ○1 analyzed the market demand for social media account hacking services, then ○2 - ○3 devised a to combat the evolving cybercrimes. Based on the large-scale method to develop cracking tools exploiting a cookie vulnerabil- data collected from different underground forums (i.e., Hack Fo- ity to perform brute-force attacks on social media accounts, later rums, Nulled) and the pre-labeled ground-truth, comprehensive ○4 purchased some compromised Facebook accounts to test his experimental studies are performed to validate the effectiveness developed tools, and thus ○5 - ○7 sold and monetized his hacking of iDetective in key player identification by comparisons with services. We also found that “Ban***s” shared his developed crack- baselines. Although we use underground forums as a showcase, ing tools with other Hack Forums users for free, who could further the proposed method and the developed system can be easily use it for profits. The skilled individuals like “Ban***s” are heavily expanded to other social media platforms. relied upon by peers within the communities (e.g., novice hackers or newcomers can gain knowledge from them to cultivate their own specialties). (2) Non-key players: Other users including vendors, buyers, victims, technical enthusiasts, administrators and modera- tors are categorized as non-key player. Considering the vital role of key players play in the value chain (i.e., their vulnerability ex- ploiting capability, technical skills, cashing channel, and influence on others), it is important to identify key players in underground forums to facilitate the deployment of effective countermeasures to disrupt the illicit activities. Toward this goal, human analysts need to continually spend a multitude of time to keep the latest statuses and variances of the activities in underground forums under obser- vation. This calls for novel tools and methodologies to automate the identification of key players to enable the law enforcement and security practitioners to devise effective interventions. To address the above challenges, in this paper, we design and de- velop an intelligent system called iDetective to automate the analysis of underground forums for key player identification. In iDetective, we first introduce an attributed heterogeneous information network (AHIN) [32] to represent the rich relationship among users, threads, replies, comments and sections, and then use a meta-path based approach [33] to incorporate higher-level semantics to build up relatedness over users in underground forums. Then, we propose Player2Vec to efficiently learn node (i.e., underground forum user) representations in AHIN for key player identification. In Player2Vec, (1) we first map the constructed AHIN to a multi-view network which consists of K single-view attributed graphs encoding the re- latedness over users depicted by K designed meta-paths; and then (2) we employ graph convolutional network (GCN) to learn embed- Figure 1: Participants in the value chain in Hack Forums. dings of each single-view attributed graph; later, (3) an attention mechanism is designed to fuse different embeddings learned based The rest of the paper is organized as follows. Section 2 presents on different single-view attributed graphs for final representations. the system overview of iDetective. Section 3 introduces our proposed The major contributions of our work are summarized as follows: method in detail. Based on the large-scale data collections from different underground forums and the pre-labeled ground-truth, • We propose a novel yet natural feature representation to depict Section 4 systematically evaluates the performance of our developed underground forum users. In our application for key player iden- system iDetective. Section 5 discusses the related
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