关键词: 网络入侵, 粒子群算法, [K]均值算法, 核极限学习机, 10折交叉验证

Abstract: At present, some problems such as high dimension of data, large amount of data and difficult training appear in intrusion detection system. The use of Kernel Extreme Learning Machine（KELM） algorithm in intrusion detection system can make intrusion detection system adapt to the training of a large number of high dimensional data, and learning speed of the system is quick without adjusting the input value of the network, reducing the training difficulty of detection system. However, the imbalance of the invasion data sets and the interference of noise directly affect the performance of KELM. Therefore, for dealing well with the invasion of data sets, intrusion detection algorithm based on IPMeans-KELM is proposed. Firstly, the algorithm uses improved PSO to optimize the?k-means?algorithm（IPMeans）, which increases aggregation of the same data type. Next, the processed data are split with 10-CV and ten of data are trained in turn for KELM classifier. Test the data by trained KELM classifier, and then output the average detection rate. If the test result does not meet the expected conditions, the cycle is processed until the condition is meet. Finally, it shows that the method effectively improves the intrusion detection rate while reducing the false alarm rate with doing comparison experiments on Matlab.

Key words: network intrusion detection, Particle Swarm Optimization, K-means, Kernel Extreme Learning Machine（KELM）, 10-CV