Research of Accelerating K-Means Algorithm Based on New Particle Swarm Optimization for Intrusion Detection
Xiao Li-zhon · Jisuanji fangzhen · 2014
To overcome deficiency of global search ability for K-Means algorithm impacted by initial centroids in intrusion detection and premature convergence for particle swarm optimization algorithm, an accelerating K-Means algorithm based on new particle swarm optimization(NPSO-AKM) was proposed. In the algorithm, K-Means and particle swarm optimization algorithms were improved and integrated, which provided the algorithm with relatively higher processing speed and better global convergence. To address the features of clustering algorithm for NPSO-AKM, an intrusion detection model based on NPSO-AKM was designed. For experimental dataset, the cross-method was achieved to build high-quality training dataset. The experiments show the model has relatively good and fast global convergence, and can get satisfied detection rate and false alarm rate in intrusion detection.