Design an Efficient System for Intrusion Detection via Evolutionary Fuzzy System

Abdolhamid Momenzadeh, H. Javadi, M. Dezfouli
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引用次数: 5

Abstract

In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Evolutionary Fuzzy System hybridizes the approximate reasoning method of fuzzy systems with the learning capability of Evolutionary Algorithms. The objective of this paper is to demonstrate the ability of Evolutionary Fuzzy System to deal with intrusion detection classification problem as a new real-world application area. The Evolutionary Fuzzy System would be capable of extracting accurate fuzzy classification rules from network traffic data and applies them to detect normal and intrusive behaviors in computer network. Experiments were performed with KDDCup99 intrusion detection benchmark dataset which has information on computer networks, during normal and intrusive behaviors. The results illustrate that the proposed algorithm achieves more accurate intrusion detection system than several well-known and new methods.
利用进化模糊系统设计一种高效的入侵检测系统
在之前的一些研究中,模糊系统解决不同类型问题的能力已经得到了证明。进化模糊系统将模糊系统的近似推理方法与进化算法的学习能力相结合。本文的目的是证明进化模糊系统处理入侵检测分类问题的能力作为一个新的实际应用领域。进化模糊系统能够从网络流量数据中提取出准确的模糊分类规则,并将其应用于计算机网络中正常和侵入行为的检测。利用具有计算机网络信息的KDDCup99入侵检测基准数据集,对正常行为和入侵行为进行了实验。结果表明,该算法比几种已知的新方法实现了更精确的入侵检测系统。
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