Harvesting Large Textual and Multimedia Data to Detect Illegal Activities on Dark Web Marketplaces.

During the last decades, the dark web has become the ground for criminal activities, enabling for illegal content sharing, as well as marketplaces selling drugs and firearms. The 2023 Internet Organised Crime Threat Assessment (IOCTA) of Europol’s European Cybercrime Centre (EC3) presented the dark web as the one of the top crime ecosystems. Analyzing the .onion sites hosting marketplaces is of interest to law enforcement, security researchers, and big data analysts. The capability of automatically harvesting web content from web servers enables Law Enforcement Agencies (LEAs) to collect and preserve data prone to serve as potential clues or evidence in investigations. The use of sophisticated protocols and the inherent complexity of the Dark Web makes it difficult for security agencies to identify and investigate these activities through conventional methods. The sheer size, unpredictable ecosystem, and anonymity provided by the Dark Web are the essential confrontations to trace the criminals. Therefore, it is a crucial step to discover the potential solutions towards cyber-crimes evaluating the sailing Dark Web crime threats. In this paper, we devise Artificial Intelligence and Big Data processing to extract insights and investigate how the Dark Web facilitates crime and dynamically maintains marketplaces with illegal goods exchange. The scientific contribution of this paper entails novel textual and multimedia analytics over large collections of data to collect evidence for further investigations and support visual reporting and alerting mechanisms. The conclusions include practical implications of Dark Web content retrieval and archival, such as investigation clues and evidence, and related future research topics.