S.U.S. You're SUS!—Identifying influencer hackers on dark web social networks. (April 2023) - Paracha, Anum Atique; Arshad, Junaid; Khan, Muhammad Mubashir
Abstract: Dark web is an obscured part of the Internet, specifically used for sharing exploits, data breaches, and other means of cybercrime. Dark web forums provide opportunities to share such data and exploits and assign user reputation and credibility through participation in discussions and sharing data, exploits, and hacks. Such activities can help develop metrics to enable identification of influential mal-actors facilitating efficient and effective defense against emerging cyber threats, particularly zero-day exploits. This paper proposes an AI-inspired framework to identify influencers on dark web social networks (INSPECT) through intelligent analysis of user-profiles, interactions, and other activities. INSPECT framework leverages Feature Engineering, Social Network Analysis, Semantic Analysis, and K-means clustering and calculates an influencer score representing the users' significance within these forums. INSPECT has been evaluated using CrimeBB dataset comprising user profiles and activities within dark web forums to assess its effectiveness in identifying influential users on the dark web forums.