Understanding the Topological Structure and Semantic Content of Darknet Communities

For over a decade Darknet has been gaining tremendous popularity proportional to the growing concerns fostered by lack of anonymity and privacy on the World Wide Web. In the recent years, illegitimate use of the Darknet has resulted into investigation in the research community that is analogous to a domino effect further adding to popularity of this type of network. Unfortunately, higher percentages have been attributed to the illegitimate use of the Darknet rather than to the legitimate use. This is because researchers of the Darknet communities have relied on the knowledge obtained through the use of Breadth First Search crawling algorithm. Crawling makes up the main step in the exploration of these communities. Crawling is also an effective method to understand the topological and semantic structure of the Darknet communities. The algorithms chosen to crawl thus, decide the knowledge obtained from these communities. This thesis demonstrates how these crawling algorithms spread out over the Darknet communities and how this affects what and how much we know about them. The considerations presented eliminate the skew in the representation of Darknet communities. The knowledge explored through the behavior of BFS, DFS and RFS algorithms have been presented in this thesis.Electrical Engineering, Mathematics and Computer ScienceTelecommunicationsCybersecurit