LEVERAGING AI AND ML TO INNOVATE FORENSIC FRAMEWORKS FOR THE IDENTIFICATION OF ILLICIT OPERATIONS AND EXTRACTION OF DIGITAL ARTIFACTS WITHIN DEEP WEB AND DARK WEB ENVIRONMENTS

Significant chunks of the internet are made up of the deep web and dark web. The deep web refers to content that is not indexed by conventional search engines, while the dark web is a subset that is purposefully hidden and only accessible with the use of specialist tools like Tor. Academic databases, research papers, and private chat platforms are examples of respectable content found on the deep web, although the dark web has become notorious for harbouring illegal activity. Cybercrime, illicit drug markets, human trafficking, arms dealing, and other criminal operations that take advantage of the anonymity offered by Tor and VPNs are examples of these activities. For cybersecurity specialists, law enforcement organizations, and digital forensics specialists, looking into illicit activity in these areas is a significant task. Conventional forensic methods, which frequently depend on content analysis or IP address identification, are useless against anonymizing technology. To find digital evidence in these elusive regions, however, new developments in forensic techniques such as blockchain forensics, traffic fingerprinting, and machine learning techniques—offer encouraging alternatives. This study examines these methods and suggests a thorough framework for dark web and deep web digital forensics.