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).
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© 2011 Springer-Verlag Berlin Heidelberg
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Hou, Y., Zheng, X.F. (2011). SVM Based MLP Neural Network Algorithm and Application in Intrusion Detection. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_41
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DOI: https://doi.org/10.1007/978-3-642-23896-3_41
Publisher Name: Springer, Berlin, Heidelberg
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