Principal component neural networks based intrusion feature extraction and detection using SVM

Authors:
Hai-Hua Gao;Hui-Hua Yang;Xing-Yu Wang
Affiliations:
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China;School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China;School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

Very little research on feature extraction has been taken in the field of network intrusion detection. This paper proposes a novel method of applying principal component neural networks for intrusion feature extraction, and then the extracted features are employed by SVM for classification. The adaptive principal components extraction (APEX) algorithm is adopted for the implementation of PCNN. The MIT's KDD Cup99 dataset is used to evaluate the proposed method compared to SVM without application of feature extraction technique, which clearly demonstrates that PCNN-based feature extraction method can greatly reduce the dimension of input space without degrading or even boosting the performance of intrusion detection system.