Exploring Anomalies in Dark Web Activities for Automated Threat Identification

The dark web is an in-built platform for hackers, cybercriminals, and malicious users to continue illegal activities beyond the reach of traditional law enforcement agencies. This paper discusses why unusual behaviour in the dark web is not apparent and offers an automated threat model with anomaly detection methods. Such discrepancies may be equivalent to aberrant user actions, not standard user patterns of transactions, or other criminal activity linked with criminal activities. Traditionally, dark web surveillance was a sluggish endeavour dependent largely on human entities to sift through terabytes of information. Automation technologies, however, can accurately identify such risks. Data used in this research are publicly accessible dark web data such as forum posts, market transactions, and network traffic data, which were preprocessed before being corrected and normalized. Python was employed as the first-line tool for model training, testing, and result analysis, employing libraries like Scikit-learn for machine learning, TensorFlow for deep models, and Graphviz for visualizing graphs. The approach employed in this paper employs unsupervised learning for anomaly detection and classification algorithms to detect threats.