Abstract
Colinearity and latent relation among different input features of net work intrusion detection system (IDS) have to be addressed. The strong nonlinearity and uncertain mapping between input features and network intrusion behaviors lead to difficulty to built effective detection model for IDS. In this paper, a new supervised nonlinear latent feature extraction and fast machine learning algorithm based on global optimization strategy is proposed to solve these problems. Specifically, for diminishing colinearity among input variables, kernel partial least squares (KPLS) algorithm is employed to extract nonlinear latent features. Then, regularized random weights neural networks (RRWNN) is utilized to construct the intrusion detection model. To optimize the proposed system, the modeling parameters of KPLS and RRWNN are selected in terms of global optimization. Experiments on KDD99 data show that the proposed approach is effective.
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Acknowledgment
This work is partially supported by the post doctoral National Natural Science Foundation of China (2013M532118, 2015T81082), National Natural Science Foundation of China (61573364, 61273177, 61305029, 61503066), State Key Laboratory of Synthetical Automation for Process Industries, China National 863 Projects (2015AA043802), and the Project Funded by the Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET) fund.
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Tang, J., Zhuo, L., Jia, M., Sun, C., Shi, C. (2016). Supervised Nonlinear Latent Feature Extraction and Regularized Random Weights Neural Network Modeling for Intrusion Detection System. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10039. Springer, Cham. https://doi.org/10.1007/978-3-319-48671-0_31
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DOI: https://doi.org/10.1007/978-3-319-48671-0_31
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