Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies  

作　　者：Adel Alshamrani Abdullah Alshahrani 

机构地区：[1]Department of Cybersecurity,College of Computer Science and Engineering,University of Jeddah,Jeddah,Saudi Arabia [2]Department of Computer Science and Artificial Intelligence,College of Computer Science and Engineering,University of Jeddah,Jeddah,Saudi Arabia

出　　处：《Intelligent Automation & Soft Computing》2023年第6期2757-2771,共15页智能自动化与软计算（英文）

基　　金：This work is funded by the Deanship of Scientific Research(DSR);the University of Jeddah,under Grant No.(UJ-22-DR-1).

摘　　要：The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems.In this paper,we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber defense.We present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning(MARL).Our proposed approach is inspired by the multiarmed bandits(MAB)learning technique for multiple agents to cooperate in decision making or to work independently.We study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their system.We find that this game can be modeled from an individual player’s perspective as a restless MAB problem.We discover further results when the MAB takes the form of a pure birth process,such as a myopic optimal policy,as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.

关 键 词：Multiarmed bandits reinforcement learning MULTIAGENTS intrusion detection systems 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]