Discovering Emerging Threats in the Hacker Community: A Nonparametric Emerging Topic Detection Framework

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Publication History

Received: July 26, 2018
Revised: June 18, 2019; April 13, 2020; February 16, 2021; December 15, 2021
Accepted: February 25, 2022
Published Online as Articles in Advance: November 28, 2022
Published in Issue: Forthcoming

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The prevalence and rapid growth of cybercrime are largely attributed to hacker communities on the dark web, where cybercriminals extensively exchange hacking resources, share hacking knowledge, and organize cyberattacks. Such streams of hacker-generated content constitute an invaluable data source for developing threat intelligence that can inform organizations of cybersecurity risks and facilitate proactive cyber defense. Drawing upon the design science paradigm, we propose a novel nonparametric emerging topic detection (NPETD) framework for detecting emerging topics in streams of hacker-generated content. Our framework extends the state-of-the-art nonparametric topic model to inductively model topics without having to specify the number of topics a priori. Moreover, our framework features an efficient algorithm to jointly infer topics and detect topic emergence. We conducted experiments to rigorously evaluate the effectiveness and efficiency of our framework in comparison with the state-of-the-art baseline methods. Our framework outperformed the baseline methods in detecting the listings of emerging threats in darknet marketplaces on recall, F-measure, topic coherence, and processor time. The practical utility of our framework is further demonstrated in a major hacker forum, where we identified several notable emerging topics with important implications for victim companies and law enforcement. The proposed framework contributes to cybersecurity, topic detection and tracking, and design science.

Additional Details
Author Weifeng Li and Hsinchun Chen
Year 2022
Volume 46
Issue 4
Keywords Cybersecurity, hacker community, topic detection and tracking, emerging topic detection, nonparametric topic model, design science
Page Numbers 2337-2350
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