基于人工智能的网络安全攻击缓解的马尔科夫博弈模型
A Markov Game Model for AI-based Cyber Security Attack Mitigation
摘要：
新一代网络威胁利用先进的人工智能辅助方法，使其能够发起多阶段，动态和有效的攻击。当前的网络防御系统在防御这些新的和正在出现的威胁方面遇到了各种挑战。通过博弈论模型对人工智能辅助的威胁进行建模，可以帮助防御者选择最佳策略来应对攻击，并做出明智的决策来减轻攻击的影响。本文首先探讨了新一代威胁的当前最新技术水平，其中深度神经网络等AI技术被用于攻击者，并讨论了进一步的挑战。本文提出了一个马尔可夫动态博弈模型，在云计算环境下，攻击者利用人工智能技术，通过寻找最短攻击路径发起高级攻击，并利用CVSS度量来量化该零和博弈模型的价值，以便于决策。
英文摘要：
The new generation of cyber threats leverages advanced AI-aided methods, which make them capable to launch multi-stage, dynamic, and effective attacks. Current cyber-defense systems encounter various challenges to defend against such new and emerging threats. Modeling AI-aided threats through game theory models can help the defender to select optimal strategies against the attacks and make wise decisions to mitigate the attack's impact. This paper first explores the current state-of-the-art in the new generation of threats in which AI techniques such as deep neural network is used for the attacker and discusses further challenges. We propose a Markovian dynamic game that can evaluate the efficiency of defensive methods against the AI-aided attacker under a cloud-based system in which the attacker utilizes an AI technique to launch an advanced attack by finding the shortest attack path. We use the CVSS metrics to quantify the values of this zero-sum game model for decision-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
