Research On Intrusion Detection Methods Based On Clustering And Support Vector Machines

With the rapid development of computer technology and Internet technology,it has made a great impact on our lives.As a result,our reliance on computer networks has become increasingly strong.However,issues concerning network security have always been the focus of the entire society.At present,more mature network security protection technologies include: firewalls,secure routing,and data encryption and related technologies.However,these technologies are static network security protection methods,and it is difficult to meet the current needs of people for network security performance.Intrusion detection,as a new technical means of network security defense,is characterized by its proactive defense capabilities and can assist traditional network security technologies such as firewalls to enhance the security of network systems.Detection technology has become the focus of research in the field of network security.In order to discover intrusions in a timely and effective manner,products related to intrusion detection are developing in an intelligent and distributed manner.In this dissertation,traditional intrusion detection algorithms have the problems of low detection efficiency,high false alarm rate,and can not effectively identify the types of attacks.Clustering algorithms and support vector machine algorithms are applied to intrusion detection to improve the detection performance of intrusion detection systems..The main contents of this dissertation are:(1)In order to improve the efficiency of intrusion detection and reduce false alarm rate,this dissertation applies clustering algorithm to intrusion detection.Firstly,the clustering algorithm is summarized.Secondly,the K-means algorithm based on partition is introduced.According to the existence of the clustering center point,the algorithm is improved correspondingly.The improved K-means is proposed.Means intrusion detection method.(2)In order to improve the efficiency of intrusion detection and reduce false alarm rate,this dissertation applies clustering algorithm to intrusion detection.Firstly,the clustering algorithm is summarized.Secondly,the K-means algorithm based on partition is introduced.According to the existence of The clustering center point,the algorithm is improved correspondingly.The improved K-means is proposed.Means intrusion detection method.(3)This dissertation,using the experimental data set KDD CUP99 in intrusion detection to simulate the proposed intrusion detection method,the experimental resultsshow that the intrusion detection method proposed in this dissertation has a certain improvement in detection rate and false positive rate.An effective network security protection method.