Abstract
As a crucial issue in computer network security, anomaly detection is receiving more and more attention from both application and theoretical point of view. In this paper, a novel anomaly detection scheme is proposed. It can detect anomaly network traffic which has extreme large value on some original feature by the major component, or does not follow the correlation structure of normal traffic by the minor component. By introducing kernel trick, the non-linearity of network traffic can be well addressed. To save the processing time, a simplified version is also proposed, where only major component is adopted. Experimental results validate the effectiveness of the proposed scheme.
This work is supported by National Fundamental Research Development (973) under the contract 2003CB314805.
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Tong, H. et al. (2005). Anomaly Internet Network Traffic Detection by Kernel Principle Component Classifier. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_77
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DOI: https://doi.org/10.1007/11427469_77
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