Application of self-organizing feature map neural network based on K-means clustering in network intrusion detection

Abstract

Due to the widespread use of the Internet, customer information is vulnerable to computer systems attack, which brings urgent need for the intrusion detection technology. Recently, network intrusion detection has been one of the most important technologies in network security detection. The accuracy of network intrusion detection has reached higher accuracy so far. However, these methods have very low efficiency in network intrusion detection, even the most popular SOM neural network method. In this paper, an efficient and fast network intrusion detection method was proposed. Firstly, the fundamental of the two different methods are introduced respectively. Then, the self-organizing feature map neural network based on K-means clustering (KSOM) algorithms was presented to improve the efficiency of network intrusion detection. Finally, the NSL-KDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than K-means method. The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.