入侵檢測異常數(shù)據(jù)的分類與可視化研究
[Abstract]:With the development of network technology, the security problem becomes more and more prominent. In the face of network security problems, the traditional intrusion detection technology has many defects such as low detection rate and low detection efficiency in multi-classification of abnormal data. In order to solve the problem of low detection rate, this paper improves the binary tree support vector machine algorithm based on clustering algorithm, and the detection rate is improved. In order to solve the problem of low detection efficiency, this paper improves the detection efficiency by using MapReduce technology on the basis of the improved algorithm. In this paper, an intrusion detection anomaly data classification and visualization system based on improved algorithm is designed and implemented. The system consists of four modules: intrusion detection data acquisition module, intrusion detection anomaly data statistical analysis module, intrusion detection data visualization module and system management module. The intrusion detection data acquisition module is mainly responsible for the acquisition of intrusion data, and the intrusion detection anomaly data statistical analysis module mainly uses the improved multi-classification algorithm to classify the abnormal data. Intrusion detection data visualization module is mainly used for data collation, query, statistics and graphic display. The system management module is mainly used for the configuration of alarm rules, alarm configuration and user authority management. The purpose of this paper is to collect the intrusion detection data together, through the automatic processing of the statistical analysis module, and finally to use Highcharts technology to display the data on the visual interface, which provides the judgment basis for preventing and reducing the network intrusion behavior.
【學位授予單位】:華北電力大學(北京)
【學位級別】:碩士
【學位授予年份】:2017
【分類號】:TP393.08
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