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Computer Science > Cryptography and Security

arXiv:2408.01310 (cs)
[Submitted on 2 Aug 2024 (v1), last revised 13 Aug 2024 (this version, v3)]

Title:PsybORG+: Modeling and Simulation for Detecting Cognitive Biases in Advanced Persistent Threats

Authors:Shuo Huang, Fred Jones, Nikolos Gurney, David Pynadath, Kunal Srivastava, Stoney Trent, Peggy Wu, Quanyan Zhu
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Abstract:Advanced Persistent Threats (APTs) bring significant challenges to cybersecurity due to their sophisticated and stealthy nature. Traditional cybersecurity measures fail to defend against APTs. Cognitive vulnerabilities can significantly influence attackers' decision-making processes, which presents an opportunity for defenders to exploit. This work introduces PsybORG$^+$, a multi-agent cybersecurity simulation environment designed to model APT behaviors influenced by cognitive vulnerabilities. A classification model is built for cognitive vulnerability inference and a simulator is designed for synthetic data generation. Results show that PsybORG$^+$ can effectively model APT attackers with different loss aversion and confirmation bias levels. The classification model has at least a 0.83 accuracy rate in predicting cognitive vulnerabilities.
Subjects: Cryptography and Security (cs.CR)
Cite as: arXiv:2408.01310 [cs.CR]
  (or arXiv:2408.01310v3 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2408.01310

Submission history

From: Shuo Huang [view email]
[v1] Fri, 2 Aug 2024 15:00:58 UTC (1,495 KB)
[v2] Mon, 12 Aug 2024 15:21:26 UTC (525 KB)
[v3] Tue, 13 Aug 2024 14:00:25 UTC (525 KB)
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