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<p>The rapid proliferation of complex information systems (IS) has been met by an ever-increasing number of attacks that can cause irreparable cyber breaches. These attacks are frequently discussed and disseminated in large international hacker forums. Prevailing approaches for studying hacker forum structures often identify key hackers but do not study how their removal changes information diffusion. In this study, we adopted the computational design science paradigm to develop a novel information technology artifact called the HackerVote framework. HackerVote incorporates network science principles and node centrality measures to maximize the disruption of information propagation in hacker communities. We rigorously evaluated the proposed HackerVote against state-of-the-art node centrality attack methods. The results suggest that the proposed HackerVote outperforms benchmark methods based on four established network measures: (1) average inverse distance, (2) average clustering coefficient, (3) largest connected component size, and (4) spectral radius. We also demonstrated HackerVote’s proof-of-value with an in-depth case study simulating the removal of key hackers from a long-standing international hacker forum. Our proposed HackerVote framework provides important practical implications for law enforcement officials regarding targeted takedown strategies.</p>

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A Computational Design Framework for Targeted Disruption of Hacker Communities

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Benjamin Ampel

摘要

The rapid proliferation of complex information systems (IS) has been met by an ever-increasing number of attacks that can cause irreparable cyber breaches. These attacks are frequently discussed and disseminated in large international hacker forums. Prevailing approaches for studying hacker forum structures often identify key hackers but do not study how their removal changes information diffusion. In this study, we adopted the computational design science paradigm to develop a novel information technology artifact called the HackerVote framework. HackerVote incorporates network science principles and node centrality measures to maximize the disruption of information propagation in hacker communities. We rigorously evaluated the proposed HackerVote against state-of-the-art node centrality attack methods. The results suggest that the proposed HackerVote outperforms benchmark methods based on four established network measures: (1) average inverse distance, (2) average clustering coefficient, (3) largest connected component size, and (4) spectral radius. We also demonstrated HackerVote’s proof-of-value with an in-depth case study simulating the removal of key hackers from a long-standing international hacker forum. Our proposed HackerVote framework provides important practical implications for law enforcement officials regarding targeted takedown strategies.