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Improved Clustering for Intrusion Detection by Principal Component Analysis with Effective Noise Reduction

Abstract : PCA (Principal Component Analysis) is one of the most wildly used dimension reduction technique, which is often applied to identify patterns in complex data of high dimension [1]. In GA-KM [2], we have proposed GA-KM algorithm and have experimented using KDD-99 data set. The result showed GA-KM is efficient for intrusion detection. However, due to the hugeness of the data set, the experiment needs to take a long time to finish. To solve this deficiency, we combine PCA and GA-KM in this paper. The goal of PCA is to remove unimportant information like the noise in data sets which have high dimension, and retain the variation present in the original dataset as much as possible. The experimental results show that, compared to GA-KM [2], the proposed method is better in computational expense and time (through dimension reduction) and is also better in intrusion detection ratios (through noise reduction).
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Lu Zhao, Ho-Seok Kang, Sung-Ryul Kim. Improved Clustering for Intrusion Detection by Principal Component Analysis with Effective Noise Reduction. 1st International Conference on Information and Communication Technology (ICT-EurAsia), Mar 2013, Yogyakarta, Indonesia. pp.490-495, ⟨10.1007/978-3-642-36818-9_55⟩. ⟨hal-01480208⟩

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