Abstract
In this study, we propose a new approach to determine intrusions of network in real-time based on statistical process control technique and kernel null space method. The training samples in a class are mapped to a single point using the Kernel Null Foley-Sammon Transform. The Novelty Score are computed from testing samples in order to determine the threshold for the real-time detection of anomaly. The efficiency of the proposed method is illustrated over the KDD99 data set. The experimental results show that our new method outperforms the OCSVM and the original Kernel Null Space method by 1.53% and 3.86% respectively in terms of accuracy.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Truong, T.H., Ta, P.B., Nguyen, Q.T., Du Nguyen, H., Tran, K.P. (2019). A Data-Driven Approach for Network Intrusion Detection and Monitoring Based on Kernel Null Space. In: Duong, T., Vo, NS., Nguyen, L., Vien, QT., Nguyen, VD. (eds) Industrial Networks and Intelligent Systems. INISCOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 293. Springer, Cham. https://doi.org/10.1007/978-3-030-30149-1_11
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DOI: https://doi.org/10.1007/978-3-030-30149-1_11
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