Computer Intrusion Detection Using an Iterative Fuzzy Rule Learning Approach

M. S. Abadeh, J. Habibi
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引用次数: 13

Abstract

The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). The objective of this paper is to extract fuzzy classification rules for intrusion detection in computer networks. The proposed method is based on the iterative rule learning approach (IRL) to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The performance of final fuzzy classification system has been investigated using intrusion detection problem as a high-dimensional classification problem. Results show that the presented algorithm produces fuzzy rules, which can be used to construct a reliable intrusion detection system.
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基于迭代模糊规则学习方法的计算机入侵检测
监视计算机系统或网络中发生的事件并分析其入侵迹象的过程被称为入侵检测系统(IDS)。本文的目的是提取用于计算机网络入侵检测的模糊分类规则。提出了一种基于迭代规则学习的模糊规则库系统设计方法。模糊规则库是以增量方式生成的,因为进化算法每次优化一个模糊分类器规则。将入侵检测问题作为高维分类问题,研究了最终模糊分类系统的性能。结果表明,该算法生成的模糊规则可用于构建可靠的入侵检测系统。
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