Visible to the public Employing social network analysis to dark web communities

TitleEmploying social network analysis to dark web communities
Publication TypeConference Paper
Year of Publication2022
AuthorsNikoletos, Sotirios, Raftopoulou, Paraskevi
Conference Name2022 IEEE International Conference on Cyber Security and Resilience (CSR)
KeywordsAnalytical models, Computer crime, dark web, data mining, directed graphs, Human Behavior, human factors, Key nodes, organizational aspects, pubcrawl, search engines, social interactions, social network analysis, social networking (online)
Abstract

Deep web refers to sites that cannot be found by search engines and makes up the 96% of the digital world. The dark web is the part of the deep web that can only be accessed through specialised tools and anonymity networks. To avoid monitoring and control, communities that seek for anonymization are moving to the dark web. In this work, we scrape five dark web forums and construct five graphs to model user connections. These networks are then studied and compared using data mining techniques and social network analysis tools; for each community we identify the key actors, we study the social connections and interactions, we observe the small world effect, and we highlight the type of discussions among the users. Our results indicate that only a small subset of users are influential, while the rapid dissemination of information and resources between users may affect behaviours and formulate ideas for future members.

DOI10.1109/CSR54599.2022.9850325
Citation Keynikoletos_employing_2022