International Research Journal of Education and Technology

With the rapid evolution of cyber threats, cybercriminal activities increasingly originate and operate within hidden online environments known as the Dark Web. Traditional cybersecurity systems primarily focus on detecting attacks after they impact networks or systems. However, many cyberattacks are planned, discussed, and traded in underground forums before execution. Dark Web Intelligence aims to proactively identify emerging threats by monitoring, collecting, and analyzing data from Dark Web marketplaces, hacker forums, encrypted chat platforms, and illicit marketplaces.

This journal presents a comprehensive study on Dark Web Intelligence for Early Cybercrime Detection, including its architecture, data collection mechanisms, analytical methodologies, machine learning techniques, and real-world applications. It discusses how Natural Language Processing (NLP), graph analysis, anomaly detection, and deep learning models such as