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Journal of Electrical and Computer Engineering
Volume 2016 (2016), Article ID 3095971, 6 pages
Research Article

SVM Intrusion Detection Model Based on Compressed Sampling

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

Received 2 October 2015; Accepted 20 January 2016

Academic Editor: Michele Vadursi

Copyright © 2016 Shanxiong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.