Research Of Spectral Clustering And Application On Intrusion Detection

With the scale and technology of network increased, the risky of network intrusion is accompanied by its development, which has become a global problem. It is very important to find the behaviors of network intrusion skilled and efficiently. The passive strategy used by traditional network security technologies can not be a good solution to this problem. Traditional network security technologies have been difficult to adapt the constant intrusion of advanced. In this context, intrusion detection has emerged, which is another network security technology after firewall and data encryption. Compared to the passive strategy of traditional network security technologies, it takes initial detect strategy, which is a good supplement to traditional technologies.Data mining is the process of excavating potential knowledge from mass data. The combination of data mining and intrusion detecting enables the intrusion detection system to have the ability of self-study and to have a better dealing with a vast amount of data as well as to enhance the detecting ability and lighten security managers' work. The combination is practical and conforms to the trend of the development of intrusion detection system. Clustering is a typical unsupervised learning technique that can build intrusion detection model and detect anomaly records in unlabeled dataset. Therefore clustering has practical meaning in anomaly detection field and is of great value in promoting intrusion detection system.Spectral clustering algorithms are newly developing technique in recent years.Unlike the traditional clustering algorithms,these apply spectral graph theory to solve the clustering of non-convex sphere of sample spaces,So that they can be converged to global optimal solution. Based on the above research background, this paper creatively applies the spectral clustering algorithm on intrusion detection, to improve the efficiency of intrusion detection. This paper introduces the research background of intrusion detection, significance, current development status and data mining in intrusion detection, focusing on the superiority the cluster analysis method applied to of intrusion detection. Then introduces spectral clustering algorithm based on deficiencies of variety of traditional clustering algorithms on intrusion detection. In order to verify the feasibility and effectiveness of the algorithm, in this paper, KDD CUP 1999 data is set as the experimental use of Matlab for simulation. Experimental results show that intrusion detection of this algorithm has improved to some extent compared to the traditional clustering algorithm.