LKM: A LDA-Based K-Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks

Mobile sensor networks (MSNs), consisting of mobile nodes, are sensitive to network attacks. Intrusion detection system (IDS) is a kind of active network security technology to protect network from attacks. In the data gathering phase of IDS, due to the high-dimension data collected in multidimension space, great pressure has been put on the subsequent data analysis and response phase. Therefore, traditional methods for intrusion detection can no longer be applicable in MSNs. To improve the performance of data analysis, we apply K -means algorithm to high-dimension data clustering analysis. Thus, an improved K -means clustering algorithm based on linear discriminant analysis (LDA) is proposed, called LKM algorithm. In this algorithm, we firstly apply the dimension reduction of LDA to divide the high-dimension data set into 2-dimension data set; then we use K -means algorithm for clustering analysis of the dimension-reduced data. Simulation results show that LKM algorithm shortens the sample feature extraction time and improves the accuracy of K -means clustering algorithm, both of which prove that LKM algorithm enhances the performance of high-dimension data analysis and the abnormal detection rate of IDS in MSNs.