用强化学习训练能应对多种攻击者的通用防御智能体
Towards Type Agnostic Cyber Defense Agents
- 将攻防双方建模为贝叶斯博弈，统一刻画攻击者类型
- 通过强化学习实证发现多类型攻击下最优防御训练策略
- 适合研究自动化防御、智能攻防对抗的学者与工程师
随着计算在政府、工业和教育领域的普遍应用，网络安全已成为全球每个组织的关键组成部分。由于计算的普及，网络威胁逐年增长，导致网络安全人才短缺和技能差距。因此，许多网络安全产品厂商和安全机构开始借助人工智能加强防御能力。本文研究如何在自动化网络安全防御——强化学习的应用中，统一刻画攻击者与防御者类型。具体而言，我们从贝叶斯博弈的角度对攻击者与防御者类型进行建模，并利用强化学习，得出关于如何最优训练能够抵御多种攻击者类型的防御智能体的实证发现。
原文摘要 · Abstract (English)
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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