dStyle-GAN: Generative Adversarial Network based on Writing and Photography Styles for Drug Identification in Darknet Markets

Zhang, Yiming; Qian, Yiyue; Fan, Yujie; Ye, Yanfang; Li, Xin; Xiong, Qi; Shao, Fudong

doi:10.1145/3427228.3427603

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…In [28], a system named dSytle-GAN is introduced that considers both style-aware and content-based information to automate drug identification in DNMs. The work is focused on distinguishing the similarity between given pairs of drugs based on an attributed heterogeneous information network (AHIN) and a generative adversarial network (GAN).…”

Section: Literature Reviewmentioning

confidence: 99%

“…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%

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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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Section: Literature Reviewmentioning

confidence: 99%

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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“…For spatial methods, they operate the original graph directly and define convolution layers over nodes to aggregate information of local neighbors [Hamilton et al, 2017;Veličković et al, 2018]. Besides homogeneous GCN, some models Zhang et al, 2020b;Ye et al, 2020b;Ye et al, 2020a] handle heterogeneous graphs. Inspired by these studies, we build an attention-based GCN to learn node embeddings in AHIN.…”

Section: Introductionmentioning

confidence: 99%

Adapting Meta Knowledge with Heterogeneous Information Network for COVID-19 Themed Malicious Repository Detection

Qian

Zhang

et al. 2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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As cyberattacks caused by malware have proliferated during the pandemic, building an automatic system to detect COVID-19 themed malware in social coding platforms is in urgent need. The existing methods mainly rely on file content analysis while ignoring structured information among entities in social coding platforms. Additionally, they usually require sufficient data for model training, impairing their performances over cases with limited data which is common in reality. To address these challenges, we develop Meta-AHIN, a novel model for COVID-19 themed malicious repository detection in GitHub. In Meta-AHIN, we first construct an attributed heterogeneous information network (AHIN) to model the code content and social coding properties in GitHub; and then we exploit attention-based graph convolutional neural network (AGCN) to learn repository embeddings and present a meta-learning framework for model optimization. To utilize unlabeled information in AHIN and to consider task influence of different types of repositories, we further incorporate node attribute-based self-supervised module and task-aware attention weight into AGCN and meta-learning respectively. Extensive experiments on the collected data from GitHub demonstrate that Meta-AHIN outperforms state-of-the-art methods.

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Link Prediction-Based Multi-Identity Recognition of Darknet Vendors

Zou,

Hu,

et al. 2023

Lecture Notes in Computer Science

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dStyle-GAN: Generative Adversarial Network based on Writing and Photography Styles for Drug Identification in Darknet Markets

Cited by 10 publications

References 37 publications

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

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

Adapting Meta Knowledge with Heterogeneous Information Network for COVID-19 Themed Malicious Repository Detection

Link Prediction-Based Multi-Identity Recognition of Darknet Vendors

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