Intrusion Detection Using Fuzzy Stochastic Local Search Classifier

B. Bahamida, D. Boughaci
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引用次数: 2

Abstract

This paper proposes a stochastic local search classifier combined with the fuzzy logic concepts for intrusion detection. The proposed classifier works on knowledge base modeled as a fuzzy rule "if-then" and improved by using a stochastic local search. The method is tested on the Benchmark KDD'99 intrusion dataset and compared with other existing techniques for intrusion detection. The results are encouraging and demonstrate the benefit of the proposed approach.
基于模糊随机局部搜索分类器的入侵检测
本文提出了一种结合模糊逻辑概念的随机局部搜索分类器用于入侵检测。该分类器工作在基于模糊规则“if-then”的知识库上,并通过随机局部搜索进行改进。在基准KDD'99入侵数据集上对该方法进行了测试,并与其他现有的入侵检测技术进行了比较。结果是令人鼓舞的,并证明了所提出的方法的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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