Shedding Light on the Dark Web
K. G. Anilkumar, D. Muhammad Noorul Mubarak · Advances in information security, privacy, and ethics book series · 2025
The Dark Web, a hidden corner of the internet, poses a significant challenge to Law Enforcement Agencies (LEAs) due to its anonymous nature and encrypted networks. While it provides a secure platform for legitimate users, it also enables illicit activities such as cybercrime, drug trade, and terrorism. One major obstacle for LEAs is the practice of URL hopping, where Dark Web sites frequently change their URLs to evade detection. To address this issue, the authors propose a novel ensemble model that leverages machine learning techniques to analyze the content of Dark Web sites and correlate it with existing intelligence. Their system aims to improve the accuracy of tracing URL hopping on the Dark Web, ultimately enhancing the ability of LEAs to track and disrupt illicit activities. By bridging the gap between the Dark Web's anonymity and LEA's monitoring capabilities, their research contributes to a safer and more secure online environment.