VendorLink: An NLP approach for Identifying &amp; Linking Vendor Migrants &amp; Potential Aliases on Darknet Markets

Saxena, Vageesh Kumar; Rethmeier, Nils; Dijck, Gijs van; Spanakis, Gerasimos

doi:10.18653/v1/2023.acl-long.481

Cited by 2 publications

(1 citation statement)

References 61 publications

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“…This would enable law enforcement agencies to take preventive measures to enforce the law. The proposition can provide answers to questions such as: Several existing works in the literature have uncovered the uses of this archive, such as drug trafficking [19,22,[25][26][27][28], author verification [29], cryptocurrency and Bitcoin transactionrelated analysis [30][31][32][33], malware analysis [34], vendor identification [19,20,[35][36][37], social media analysis [38][39][40], and identifying services provided by DarkNet markets [41][42][43][44][45]. However, very little to no work has been done to determine prospective cybercrime by classifying the contents of Dark Web forums.…”

Section: Introductionmentioning

confidence: 99%

Towards Safe Cyber Practices: Developing a Proactive Cyber-Threat Intelligence System for Dark Web Forum Content by Identifying Cybercrimes

et al. 2023

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The untraceable part of the Deep Web, also known as the Dark Web, is one of the most used “secretive spaces” to execute all sorts of illegal and criminal activities by terrorists, cybercriminals, spies, and offenders. Identifying actions, products, and offenders on the Dark Web is challenging due to its size, intractability, and anonymity. Therefore, it is crucial to intelligently enforce tools and techniques capable of identifying the activities of the Dark Web to assist law enforcement agencies as a support system. Therefore, this study proposes four deep learning architectures (RNN, CNN, LSTM, and Transformer)-based classification models using the pre-trained word embedding representations to identify illicit activities related to cybercrimes on Dark Web forums. We used the Agora dataset derived from the DarkNet market archive, which lists 109 activities by category. The listings in the dataset are vaguely described, and several data points are untagged, which rules out the automatic labeling of category items as target classes. Hence, to overcome this constraint, we applied a meticulously designed human annotation scheme to annotate the data, taking into account all the attributes to infer the context. In this research, we conducted comprehensive evaluations to assess the performance of our proposed approach. Our proposed BERT-based classification model achieved an accuracy score of 96%. Given the unbalancedness of the experimental data, our results indicate the advantage of our tailored data preprocessing strategies and validate our annotation scheme. Thus, in real-world scenarios, our work can be used to analyze Dark Web forums and identify cybercrimes by law enforcement agencies and can pave the path to develop sophisticated systems as per the requirements.

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