电子学报2023，Vol.51Issue(11)：3033-3041,9.DOI:10.12263/DZXB.20230369
基于强化学习的自免疫动态攻击生成方法
Autoimmune Dynamic Attack Generation Method Based on Reinforcement Learning
摘要
Abstract
The approach of launching network attacks through optimal pathways has become a significant factor af-fecting the internal network security of various enterprises and organizations.Existing methods for exploring optimal attack pathways within internal networks mostly rely on attack graphs and often neglect the relationship between attack costs and benefits.Methods that utilize the Q-learning algorithm to address attack pathways suffer from low utilization of network vulnerability information.To address these issues,this paper draws inspiration from the biological immune system and pro-poses a reinforcement learning-based dynamic self-immune attack generation method.This method simulates network at-tacks by intruders on an internal network,efficiently uncovering vulnerabilities within the internal network,thereby achiev-ing self-immune defense.The proposed approach first acquires and processes internal network information,attaches weights to directed edges in the attack graph,and then employs an improved Q-learning algorithm to discover optimal at-tack pathways.It successfully identifies all optimal attack pathways,providing attack graphs and an analysis of host vulner-abilities within these pathways.Theoretical analysis and experimental results demonstrate that this method not only effi-ciently and accurately identifies optimal attack pathways but also resolves issues such as ring loops and multiple optimal at-tack pathways.By making full use of internal network vulnerabilities,it enhances self-immune security defenses.关键词
最优攻击路径/强化学习/攻击图/路径规划/内网安全Key words
optimal attack path/reinforcement learning/attack graph/path planning/intranet security分类
信息技术与安全科学引用本文复制引用
李腾,唐智亮,马卓,马建峰..基于强化学习的自免疫动态攻击生成方法[J].电子学报,2023,51(11):3033-3041,9.基金项目
国家自然科学基金(No.62272370) （No.62272370）
中国科协青年人才托举工程(No.2022QNRC001) （No.2022QNRC001）
陕西省科学技术协会青年人才托举计划(No.20210120)National Natural Science Foundation of China(No.62272370) （No.20210120）
Young Elite Scientists Sponsor-ship Program by CAST(No.2022QNRC001) （No.2022QNRC001）
Shaanxi University Science and Technology Association Youth Talent Pro-motion Project(No.20210120) （No.20210120）
