首页> 外文会议>2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications >Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems
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Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems

机译:受基于代理的人工免疫系统机器学习启发的基于主机的入侵检测系统

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摘要

An adaptable agent-based IDS (AAIDS) inspired by the danger theory of artificial immune system is proposed. The learning mechanism of AAIDS is designed by emulating how dendritic cells (DC) in immune systems detect and classify danger signals. AG agent, DC agent and TC agent coordinate together and respond to system calls directly rather than analyze network packets. Simulations show AAIDS can determine several critical scenarios of the system behaviors where packet analysis is impractical.
机译:提出了一种基于人工免疫系统危险性理论的基于适应剂的IDS(AAIDS)。通过模拟免疫系统中的树突状细胞(DC)如何检测和分类危险信号来设计AAIDS的学习机制。 AG代理,DC代理和TC代理协同工作,直接响应系统调用,而不是分析网络数据包。仿真表明,AAIDS可以确定一些无法进行数据包分析的系统行为的关键情况。

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