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SVM Intrusion Detection Model Based on Compressed Sampling


Affiliations
1 College of Computer and Information Science, Southwest University, Chongqing 400715, China
2 Chongqing City Management Vocational College, Chongqing 400055, China
 

Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling.We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy.
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  • SVM Intrusion Detection Model Based on Compressed Sampling

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Authors

Shanxiong Chen
College of Computer and Information Science, Southwest University, Chongqing 400715, China
Maoling Peng
Chongqing City Management Vocational College, Chongqing 400055, China
Hailing Xiong
College of Computer and Information Science, Southwest University, Chongqing 400715, China
Xianping Yu
College of Computer and Information Science, Southwest University, Chongqing 400715, China

Abstract


Intrusion detection needs to deal with a large amount of data; particularly, the technology of network intrusion detection has to detect all of network data. Massive data processing is the bottleneck of network software and hardware equipment in intrusion detection. If we can reduce the data dimension in the stage of data sampling and directly obtain the feature information of network data, efficiency of detection can be improved greatly. In the paper, we present a SVM intrusion detection model based on compressive sampling.We use compressed sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation. After that SVM is used to classify the compression results. This method can realize detection of network anomaly behavior quickly without reducing the classification accuracy.