Title: A Bayesian network intrusion detection algorithm based on principal component analysis and sliding window
Authors: Zuhong Feng; Jing Li; Jie Hu Meichsner
Addresses: School of Computer Science, Beifang University of Nationalities, Yinchuan 750021, China ' School of Computer Science, Beifang University of Nationalities, Yinchuan 750021, China ' Department of Computer Science and Information Technology, St Cloud State University, St Cloud, MN 56301, USA
Abstract: In traditional Bayesian network (BN) intrusion detection, it is not taken into account that the dataset has an excessive number of attributes, which leads to an excessive calculation in the process of BN structure and thus greatly affects the detection efficiency. In addition, traditional BN intrusion does not consider attacks in the detection process; instead, it just simply relies on fixed BN to test the new dataset, which has a certain impact on detection accuracy. To solve these two problems, a new BN intrusion detection technology based on principal component analysis (PCA) and sliding window is introduced in this paper. The new algorithm reduces data dimensionality and uses the detected data to update the training dataset, which relatively completely reflects the overall status of the system. The experiments show that the improved algorithm can greatly reduce the computation cost and improve the detection accuracy.
Keywords: feature selection; PCA; principal component analysis; sliding window; Bayesian networks; intrusion detection.
International Journal of Security and Networks, 2014 Vol.9 No.4, pp.216 - 221
Received: 06 May 2013
Accepted: 03 Oct 2013
Published online: 04 Dec 2014 *