Abstract:
Historically, the Dark Web has been an online area where cybercrime has grown. This area serves mainly as a secret or hidden marketplace for reportedly goods and services exchange secretly to avoid identification. Among other operations want to sell harmful items, information such as stolen, secret information. Due to the absence of programs paired with CAPTCHA security, data encryption and the complexity of the method, technology like Tor, I2P, or Freenet, and dynamic URLs make the WebCrawler completely useless for identification and investigation. This work will suggest designing and implementing an effective dark web crawler for adaptation for use in cybercriminal activities and for qualified professional forensic analysis. During this work, many varieties of elementary data processing methods were realized from seed detection, information base clustering, machine learning categorization and structural storage to protect from crawling to facilitate forensic analysis. The feature of adaptive algorithms was acknowledged and fundamentally inflexible to access and cross the barriers. Besides, it is significantly cross-network compatible which shows the potential for sufficient coverage of services for maintain privacy. From the point of view of this experimental verification, the crawler can be significantly more accurate for the detection method and certainly meaningless regarding the amount in relation to the standard method and approaches. This research breakthrough brings in real-time investigative tools that can be both precise and cost-effective, while keeping the data undoubted forensically. In a country like Bangladesh, for instance, these devices will prove an especially strong boon. Because of these results, it’s clear that crawlers do not have to operate alone. It also has ample capacity to aid law enforcement, academicians, and big business in alleviating the swiftly growing threat of cyber criminality by boosting digital resilience.
