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A clustering method in intrusion detection

Author: MaJun
Tutor: LuoGuangChun;LiXiaoYang
School: University of Electronic Science and Technology
Course: Software Engineering
Keywords: Intrusion Detection Data Mining Clustering Normal model
CLC: TP393.08
Type: Master's thesis
Year: 2008
Downloads: 61
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Abstract


With the rapid development and wide application of computer network technology, especially the rapid spread of the Internet to promote innovation and upgrade of computer and Internet technology. Human society, the degree of information is increasing, and increasing dependence on the network, and the normal of the information society, how can we ensure safe and smooth operation of computer network security is one of the most important aspects, must continue to be able to enrich strengthen and improve. Currently, the breadth and depth of the field of network interconnection continues to expand, the deepening open nature, caused by the increasing number of network systems face the threat of attacks and intrusions. This thesis is based on the research background, to carry out the network intrusion detection based on data mining, and briefly discusses the concepts and principles of intrusion detection and data mining; data mining development status and trends in intrusion detection and intrusion detection; focused on Anomaly Detection and clustering applications: misuse detection technology mainly through the establishment of a connection record feature attributes known intrusion classification algorithm to determine; major advantage of anomaly detection technology that can detect unknown attacks, anomaly detection is an important complement to misuse detection; anomaly detection is still faced with many challenges, one of the most important one is the high rate of false alarms; compared to the method of classification, clustering methods of training data requirements low artificial price relative reduction and has better adaptability, but need a better algorithm to improve its accuracy; highly accurate, real-time and adaptive multi-technology integration of intrusion detection technology is the future direction of development. Article unknown intrusion detection effectiveness and adaptability of the network environment to improve detection algorithm for the target from two important indicators of the detection rate and false alarm rate, raised only normal data modeling and characteristic clusters An incremental clustering method. The analysis of such a clustering method and experimental results show that: the algorithm can be fast clustering of large-scale network data, high detection accuracy to achieve a 96% detection rate and false detection rate of 3%, and adaptability for real-time network environment, and can be applied to improve the overall detection performance with multi-agency integrated detection system combined misuse detection. Finally, a summary of research work Problems related algorithms and improved direction for future work is prospected.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > Computer Network Security
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