Classification And Visualization Of Abnormal Data In Intrusion Detection

The development of network technology,to speed up the development of information technology,security issues have become increasingly prominent.In the face of network security issues,the traditional intrusion detection technology has many defects,such as low detection rate,slow detection efficiency and so on.In order to solve the problem of low detection rate,this paper proposes a fusion algorithm based on the two fork tree support vector machine algorithm,which improves the detection rate of the algorithm;In order to solve the problem of low detection efficiency,this paper uses MapReduce technology to further improve the algorithm,improve the detection efficiency of the algorithm.This paper designs and implements a set of intrusion detection anomaly data classification and visualization system based on the improved algorithm.The system is mainly composed of four parts,which are 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 intrusion data acquisition,multi classification algorithm of intrusion detection of abnormal data statistical analysis module mainly uses the improved classification of abnormal data,intrusion detection data visualization module is mainly responsible for the collation of data,query,statistics and chart display.The system management module is mainly used for the configuration of the alarm rules,the configuration of the alarm and the management of the user’s rights.The purpose of this paper is to collect the intrusion detection data together,through the statistical analysis of the module automated processing,and finally in the visual interface using Highcharts technology for data display,in order to prevent and reduce network intrusion behavior provides a basis for judging.