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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 method is employed to aggregate the results from multiple classifiers to achieve higher accuracy.