An intrusion detection system (IDS) detects illegal manipulations of computer systems. In intrusion detection systems, feature reduction, including feature extraction and feature selection, plays an important role in a sense of improving classification performance and reducing the computational complexity. Feature reduction is even more important when online detection, which means less computational power and fast real time delivery compared with offline detection, is needed. In this paper, independent component analysis approach is applied to feature extraction in online network intrusion detection problem. We use the KDD Cup 99 data and try to reduce its 41 features such that significant less number of features would be fed into kNN and SVM classifiers. Also, a decision fusion mathod is employed to aggregate the results from multiple classifiers to achieve higher accuracy.
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机译:入侵检测系统(IDS)检测计算机系统的非法操纵。在入侵检测系统中,特征减少,包括特征提取和特征选择,在提高分类性能和降低计算复杂性的情况下起着重要作用。在线检测时,特征减少更重要,这意味着与离线检测相比的计算能力和快速实时交付较少。本文采用独立的分量分析方法应用于在线网络入侵检测问题中的特征提取。我们使用KDD Cup 99数据,并尝试减少其41个功能,使得可以将显着较少的特征送入KNN和SVM分类器。此外,使用决策融合Mathod来聚合来自多个分类器的结果以实现更高的准确性。
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