Anomaly Intrusion Detection Based on Support Vector Machine with Mexico Hat Wavelet Kernel Function

Ping An Wang; Xu Sheng Gan; Deng Kai Yao

doi:10.4028/www.scientific.net/AMM.687-691.3897

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Anomaly Intrusion Detection Based on Support...

Anomaly Intrusion Detection Based on Support Vector Machine with Mexico Hat Wavelet Kernel Function

Abstract:

The selection of kernel function in Support Vector Machine (SVM) has a great influence on the model performance. In the paper, Mexico hat wavelet kernel is introduced to employ the kernel function of SVM, and theoretically it has be prove that, Mexico hat wavelet kernel satisfies the Merce condition, that is the necessary condition as the kernel function of SVM. Simulation on the anomaly detection shows that the capability of SVM based on Mexico hat wavelet kernel is better than that of SVM based on RBF kernel with a satisfactory result for anomaly intrusion detection.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

3897-3900

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.3897

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Online since:

November 2014

Authors:

Ping An Wang*, Xu Sheng Gan, Deng Kai Yao

Keywords:

Anomaly Intrusion Detection, Mexico Hat, Support Vector Machine (SVM), Wavelet Kernel Function

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