Intrusion Detection Algorithm Based on Simulated Annealing and K-mean Clustering
基于模拟退火与K均值聚类的入侵检测算法

Intrusion detection algorithms based on K-mean clustering have sensitive dependence on initial value and are easy to fall into local extremum.To solve this issue,a new intrusion detection scheme was presented by combing Simulated Annealing and K-mean clustering.The proposed algorithm usesSA to optimize the clustering pattern in the clustering analysis.It can achieve global optimization and better accuracy of the intrusion detection system.Moremover,parallelism of SA greatly quickened the convergence rate.Experiments were completed on KDD Cup 1999,and the results show that presented scheme has lower time consume,false positive rate,and false negative rate cimpared with intrusion detedtion systems based on K-mean clustering.