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Research of intrusion detection based on clustering and application

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Tutor: SongZhongShan
School: Central South University for Nationalities
Course: Applied Computer Technology
Keywords: Intrusion Detection,Clustering,Partitioning algorithm,Genetic Algorithms
CLC: TP393.08
Type: Master's thesis
Year:  2009
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Abstract:
K-means clustering algorithm for calculating the characteristics of fast first initial clustering of data sets , in order to identify the data clustering did not propose a new unsupervised intrusion detection method - division of genetic clustering algorithm . For clustering algorithm can be sensitive to the order of the data and the need for the shortcomings of the default number of clusters , reducing the required number of parameters . Through the establishment of the initial cluster clusters and the initial cluster using genetic algorithm to optimize the combination of two -stage approach to achieve clustering, and intrusion detection . The algorithm uses a combination of genetic algorithm to optimize the initial cluster , the first use of Gray code to encode the initial clustering clusters , then construct an appropriate fitness function . In the choice of genetic operators midnight, use a can prevent premature or operator into local minima crossover and mutation rates . Clustering algorithm to overcome the division of both sensitive to the initial value drawbacks , but also the use of genetic algorithms to solve alone in a short time is difficult to find the optimal solution for the problem . The resulting network intrusion detection system is used , so that it can accurately identify the known attacks.
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