Network Intrusion Detection Based on Improved Decision Tree

Abstract

With the rapid development of information construction, the network has been everywhere; this technology has brought a lot of convenience to people, but there are also some security problems. In order to solve these problems, many methods have been proposed, among which intrusion detection technology is an important part of solving security problems. Therefore, the traditional intrusion detection system technology is difficult to adapt to the current security system requirements, and it is necessary to put forward a more effective method. At present, most of the clustering algorithm in a large number of network data set to set the cluster number and the lack of the ability of handling character properties in network transaction this kind of situation, is because these algorithms for clustering number shows the dependence of the determination of K value is very important, with the result of clustering and clustering front is fixed, and the lack of ability to deal with focus on the characters of similar properties, k-means or are using K-center is difficult to solve. Therefore, by combining decision tree classification and ant colony clustering, this paper proposes a multi-level hybrid algorithm based on decision tree and ant colony algorithms. Experiments show that this new method is very effective in intrusion detection, with a very low false alarm rate, while maintaining a relatively acceptable false alarm rate, and can also appropriately detect unknown intrusion detection so as to improve the intrusion detection rate.