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Agriculture 4. Ali Babar, Shihao Yan. 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 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. 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. Research Methods for the Digital Humanities, Springer. Cyber Res. Internet Gov. Weimann, Going dark: Terrorism on the dark web, Stud. Cyber Criminol. Studying the online illicit drug market through the analysis of digital, physical and chemical data, Forensic Sci. Analysis of hacking related trade in the darkweb. Automated categorization of onion sites for analyzing the darkweb ecosystem. Dependable Secur. How darknet market users learned to worry more and love PGP: Analysis of security advice on darknet marketplaces. Miller, The war on drugs 2. Trust intermediary in a cryptomarket for illegal drugs. How to not get caught when you launder money on blockchain?. Gomez, G. Amoc: A multifaceted machine learning-based toolkit for analysing cybercriminal communities on the darknet. Stoddart, K. Mining the dark web: Drugs and fake ids. Crawling the hidden web. Proceedings of the Vldb, Roma, Italy. Zulkarnine, A. Surfacing collaborated networks in dark web to find illicit and criminal content. April, January Discovering topics from dark websites. Dark web portal overlapping community detection based on topic models. Countering terrorism through dark web analysis. Anomaly detection in extremist web forums using a dynamical systems approach. Dark-net ecosystem cyber-threat intelligence CTI tool. Darknet and deepnet mining for proactive cybersecurity threat intelligence. Exploring threats and vulnerabilities in hacker web: Forums, IRC and carding shops. Data driven game theoretic cyber threat mitigation. Classifying illegal activities on tor network based on web textual contents. Affect intensity analysis of dark web forums. Support Syst. Didarknet: A contemporary approach to detect and characterize the darknet traffic using deep image learning. Darknet traffic classification using machine learning techniques. Dark web forums portal: Searching and analyzing jihadist forums. Scanlon, Automatic detection of cyber-recruitment by violent extremists, Secur. Sentiment and affect analysis of dark web forums: Measuring radicalization on the internet. Dark Net Market Archives, — TF-IDF vs. Data Min. Kumar, V. Advances in Data Science and Management, Springer. Wu, Z. Future Internet, Anno-mi: A dataset of expert-annotated counselling dialogues. Recurrent neural network for text classification with multi-task learning. Medsker, L. June, January Convolutional networks and applications in vision. Neural Inf. Glove: Global vectors for word representation. Simplifying the visualization of confusion matrix. Mandrekar, Receiver operating characteristic curve in diagnostic test assessment, J. Crossref citations: 0.

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Buying Ecstasy online in Gyeongju