入侵检测的协同多智能体强化学习

Guochen Shi, Gang He
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引用次数: 4

摘要

网络入侵检测系统(NIDS)是保障通信和信息系统安全的网络安全基础设施的重要组成部分。为了提高检测效率,本文提出了一种多智能体协作强化学习,即Major-Minor-RL。该模型由一个主要代理和几个次要代理组成。主代理的作用是预测流量是否正常或异常,而次代理是主代理的辅助,帮助其纠正错误。如果主要代理人的行为与大多数次要代理人的行为不同,则最终的行为将由次要代理人决定,而在大多数情况下,最终的行为与主要代理人相同。本文在NSL-KDD数据集上对该模型进行了训练,并对训练结果进行了提升。通过与现有模型的比较,我们发现在Major-Minor-RL入侵检测系统中分类性能有了很大提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative Multi-agent Reinforcement Learning for Intrusion Detection
Network intrusion detection system (NIDS) is the essential component of cyber security infrastructure to ensure the security of communication and information systems. In this paper, a collaborative multi-agent reinforcement learning, Major-Minor-RL, is proposed to make the detection more efficient. The model consists of one major agent and several minor agents. The role of major agent is to predict whether the traffic is normal or abnormal, while minor agents are auxiliary to the major agent and help it to correct errors. If the action of major agent is different fro m the behavior of most minor agents, the final action will be determined by minor agents, while in most cases, the final action is equal to the major one. In this paper, the model has been trained on NSL-KDD dataset and the results are boosted. After comparing with the existing models, we observed much better classification performance in Major-Minor-RL intrusion detection system.
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