Intrusion detection technique has become the focus in the area of network security research. Various soft computing approaches have been applied to the intrusion detection field. The paper incorporate fuzzy logic and genetic algorithms into the classifying system based on fuzzy association rule to extract both accurate and interpretable fuzzy IF-THEN rules from network traffic data for classification, and utilize genetic algorithms to optimize the classifier, The experiments and evaluations of the proposed method were performed with the KDD Cup 99 intrusion detection dataset. Results indicate the high detection accuracy for intrusion attacks and low false alarm rate of the reliable system.
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机译：入侵检测技术已经成为网络安全研究领域的重点。各种软计算方法已经应用于入侵检测领域。本文将模糊逻辑和遗传算法结合到基于模糊关联规则的分类系统中，以从网络流量数据中提取准确且可解释的模糊IF-THEN规则进行分类，并利用遗传算法对分类器进行优化。 KDD Cup 99入侵检测数据集执行了建议的方法。结果表明，该系统对入侵攻击的检测精度高，误报率低。
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