关键词: K-means聚类, 改进的模拟退火算法, 评价函数, 入侵检测

Abstract: This paper proposes an optimized K-means clustering algorithm based on improved Simulated Annealing（SA-KM） to overcome K-means’inadequacy for global search. The proposed method is more advantageous than K-means, which is not sensitive to the initial clustering center. In order to improve clustering partition quality of SA-KM algorithm, an assessment function for assessing clustering result is presented for the proposed algorithm, which reflects intra-class distance and inter-class distance more accurately. The simulation results demonstrate that the proposed algorithm can detect various intrusion behaviors in intrusion detection, as well as maintain higher network intrusion detection rate and lower false acceptance rate of network intrusion.

Key words: K-means clustering, improved simulated annealing algorithm, assessment function, intrusion detection