基于agent人工免疫系统的机器学习启发的基于主机的入侵检测系统

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI:10.1109/INISTA.2019.8778269

C. Ou

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引用次数: 11

摘要

受人工免疫系统危险理论的启发，提出了一种基于自适应agent的入侵防御系统(AAIDS)。艾滋病的学习机制是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的。AG代理、DC代理和TC代理协同工作，直接响应系统调用，而不是分析网络报文。仿真表明，在包分析不可行的情况下，AAIDS可以确定系统行为的几个关键场景。

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Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems

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.

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来源期刊

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)

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