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

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 pres...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Huang, Shuo, Jones, Fred, Gurney, Nikolos, Pynadath, David, Srivastava, Kunal, Stoney Trent, Wu, Peggy, Zhu, Quanyan
Format: Article
Language:English
Subjects:
Bayesian analysis
Cost analysis
Cybersecurity
Decision trees
Markov chains
Multiagent systems
Reagents
Sequences
Statistical inference
Sunk cost fallacy
Synthetic data
Online Access:Get full text
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Summary: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.
ISSN:2331-8422