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Towards Type Agnostic Cyber Defense Agents
Preprint   Open access

Towards Type Agnostic Cyber Defense Agents

Erick Galinkin, Emmanouil Pountrourakis and Spiros Mancoridis
02 Dec 2024
DOI: https://doi.org/10.48550/arxiv.2412.01542
url
https://arxiv.org/abs/2412.01542View
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute,  Open

Abstract

Computer Science - Artificial Intelligence Computer Science - Computer Science and Game Theory Computer Science - Cryptography and Security Computer Science - Learning
With computing now ubiquitous across government, industry, and education, cybersecurity has become a critical component for every organization on the planet. Due to this ubiquity of computing, cyber threats have continued to grow year over year, leading to labor shortages and a skills gap in cybersecurity. As a result, many cybersecurity product vendors and security organizations have looked to artificial intelligence to shore up their defenses. This work considers how to characterize attackers and defenders in one approach to the automation of cyber defense -- the application of reinforcement learning. Specifically, we characterize the types of attackers and defenders in the sense of Bayesian games and, using reinforcement learning, derive empirical findings about how to best train agents that defend against multiple types of attackers.

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