Networks Intrusion Detection Based on K-means Clustering Algorithm and Artificial Fish Wwarm Algorithm
Yuan Fang-fan · Jisuanji fangzhen · 2013
The paper proposed a network intrusion detection model( AFSA-KCM) based on artificial fish swarm algorithm and K-means algorithm. Firstly,the sampling technique and max-min distance algorithm were used to obtain the clustering center and the clustering number,and then the artificial fish swarm was used to find the optimal clustering center and number which simulates natural feeding,cluster,and rear-end behavior. Finally the optimal intrusion detection model was built by K-means algorithm according to the optimal number and centers of cluster,and the model was tested by using KDD CUP99 data. The experimental results show that,compared with other intrusion detection models,the proposed model improves the network intrusion detection rate and network intrusion detection speed,and it can provide effective guarantee for network security.