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An efficient network intrusion detection system based on fuzzy C-means and support vector machine

机译:基于模糊C-均值和支持向量机的高效网络入侵检测系统

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摘要

Need of effective and efficient Intrusion Detection System, used the concept of hybrid approach in Iintrusion Detection System where many combination of different techniques has been used so far. In this paper, proposed hybrid approach which is the combination of Fuzzy C-Mean (FCM), a clustering technique and Support Vector Machines (SVM) will be compared with K-Means and SVM, K-Means and Naïve bayes, FCM and Naïve bayes and other existing approach related with this area. All the experiments are performed on KDD Cup 99 dataset.
机译:需要有效,高效的入侵检测系统,因此在入侵检测系统中使用了混合方法的概念,到目前为止,已经使用了多种不同技术的组合。在本文中,将提出的混合方法,即模糊C均值(FCM),聚类技术和支持向量机(SVM)的组合,与K均值和SVM,K均值和朴素贝叶斯,FCM和朴素相比较。贝叶斯和其他与此领域相关的现有方法。所有实验均在KDD Cup 99数据集上进行。

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