SVM based MLP neural network algorithm and application in intrusion detection

Authors:
Yong Hou;Xue Feng Zheng
Affiliations:
School of Information Engineering, University of Science and Technology Beijing, Beijing, China and Shan Dong Vocational College of Economics and Business, Wei fang, China;School of Information Engineering, University of Science and Technology Beijing, Beijing, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

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Abstract

This paper proposes a novel learning algorithm- SVM based MLP neural network algorithm (SVMMLP), which based on the Maximal Margin (MM) principle and take into account the idea of support vectors. SVMMLP has time and space complexities O(N) while usual SVM training methods have time complexity O(N3) and space complexity O(N2), where N is the training-dataset size. Intrusion detection benchmark datasets - NSL-KDD used in experiments that enable a comparison with other state-of-the-art classifiers. The results provide evidence of the effectiveness of our methods regarding accuracy, AUC, and Balanced Error Rate (BER).