SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis

Maneriker, Pranav; He, Yuntian; Parthasarathy, Srinivasan

doi:10.18653/v1/2021.emnlp-main.548

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…The anonymity of the dark web makes it difficult to track the identities of the buyers and sellers in these transactions. Additionally, some vendors register accounts on the same market or multiple markets to reach a larger pool of potential buyers [7,8]. In this context, researching the problem of identifying vendors' multiple identities on the dark web is particularly important.…”

Section: Introductionmentioning

confidence: 99%

Multi-Identity Recognition of Darknet Vendors Based on Metric Learning

Wang,

Hu,

et al. 2024

Applied Sciences

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Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors’ multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors’ multiple identities. This method achieved better performance with an average F1-score of 0.889 and an accuracy rate of 97.535% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors’ multiple identities.

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Section: Introductionmentioning

confidence: 99%

Multi-Identity Recognition of Darknet Vendors Based on Metric Learning

Wang,

Hu,

et al. 2024

Applied Sciences

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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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