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A Clustering Algorithm Based on Density Gravity and Application in Intrusion Detection

Author: FangZhengRu
Tutor: LiangShengZhuo
School: Nanchang University
Course: Applied Computer Technology
Keywords: Intrusion Detection Clustering method Density of gravitational
CLC: TP393.08
Type: Master's thesis
Year: 2010
Downloads: 39
Quote: 0
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Abstract


Intrusion detection , as the name suggests , is found on intrusion by a number of key points from the computer network system to gather information and analyze it , and found signs of violation of security policy behavior and attacked and make automatic response , it is not only detect intrusion from the outside , and also monitor the internal users unauthorized activities . It is considered to be the second security gate, behind a firewall is one of the core of network security technology . In recent years , intrusion detection has developed into a comprehensive disciplines . Data mining , neural networks, machine learning technologies in intrusion detection applications are increasing. With the computer technology and network technology continues to evolve , the invasion of technology is increasingly diverse and complex , the pressure brought to intrusion detection system is also growing . Data mining technology appear to provide an effective means to solve this problem , the use of data mining methods as intrusion detection data analysis techniques can be extracted from the massive security events as much as possible hidden safety information , which found that the intrusion . Therefore, data mining technology and intrusion detection technology , intrusion detection system for massive data processing capacity . Cluster analysis is more commonly used in the method of data mining . It is a typical unsupervised learning techniques , can be created directly on the unlabeled data sets intrusion detection model or abnormal data has great value for improving the detection of the intrusion detection system . In this paper, an improved clustering algorithm based on density of gravitational ( the Cluster Algorithm Density Gravity - based CADGB ) . The algorithm can automatically determine the target data set the number of clusters , can find clusters of arbitrary shape , and can filter the \Then, the design of the experimental model checking density of gravitational clustering algorithm in intrusion detection applications . Finally, in order to the with KDDCUP 99 data sets for the detection data source , the density of gravitational clustering algorithm for intrusion detection experiments , experimental results show that the algorithm higher detection rate and low false detection of intrusion data quoted rate .

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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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