S.U.S. You're SUS! - Identifying Influencer Hackers on Dark Web Social Networks
Paracha, Anum and Arshad, Junaid and Khan, Muhammad Mubashir (2023) S.U.S. You're SUS! - Identifying Influencer Hackers on Dark Web Social Networks. Computers and Electrical Engineering, 107. p. 108627. ISSN 0045-7906
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Abstract
Dark web is an obscured part of the Internet, wrapped under various layers of routing making it attractive for benign usage such as anonymity and security as well as a key platform for sharing exploits, data breaches, and other means of cybercrime. Dark web forums provide opportunities to share such data and exploits similar to the social media forums within the public Internet. Users of such forums earn reputation and credibility through their 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 defence against emerging cyber threats in general and zeroday exploits in particular. In this paper, we propose a novel framework (INSPECT) to detect influential entities through intelligent analysis of user-profiles, interactions, and activities over dark web forums. INSPECT framework involves Feature Engineering, Social Network Analysis, Text Mining, Semantic Analysis, and K-means clustering and calculates an influencer score which represents the significance of the users within the dark web forum. We have used the CrimeBB dataset comprising user profiles and activities within dark web forums to evaluate effectiveness of the INSPECT framework to identify influential users on the dark web forums.
| Item Type: | Article | ||||||
|---|---|---|---|---|---|---|---|
| Identification Number: | https://doi.org/10.1016/j.compeleceng.2023.108627 | ||||||
| Dates: |
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| Uncontrolled Keywords: | Dark web, Threat Intelligence, Social Network Analysis, Semantic Analysis, Linear Regression, Feature Engineering | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Junaid Arshad | ||||||
| Date Deposited: | 10 Feb 2023 11:18 | ||||||
| Last Modified: | 22 Mar 2023 12:00 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/14175 |
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