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.
In 2016, Peng et al. REF proposed a sampling method in the compressed sensing theory to implement feature compression for network data flow so that we can gain refined sparse representation.
SVM Intrusion Detection Model Based on Compressed Sampling
J. Electrical and Computer Engineering
J. Electrical and Computer Engineering
Computer Science