A novel Ensemble Intrusion Detection System is proposed in this study. In this system, Principle Component Analysis (PCA) and Independent Component Analysis (ICA) feature extraction approaches are used to construct two Support Vector Machine (SVM) classifiers. Then the results are combined to pursue higher performance. Because the costs of false positive error and false negative error are asymmetric in IDS, we introduce Pareto-Optimal Approach to obtain the optimal weight for the ensemble system. Experiments on the data set KDD Cup 1999 Data show that the proposed system outperforms standard SVM, PCA SVM and ICA SVM.
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机译：本研究提出了一种新颖的集成入侵检测系统。在该系统中，主成分分析（PCA）和独立成分分析（ICA）特征提取方法用于构造两个支持向量机（SVM）分类器。然后将结果合并以追求更高的性能。由于IDS中误报错误和误报错误的代价是不对称的，因此我们引入了帕累托最优方法来获得集成系统的最优权重。在KDD Cup 1999数据集上进行的实验表明，所提出的系统优于标准SVM，PCA SVM和ICA SVM。
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