Abstract
In order to improve the detection rate of network intrusion, this paper proposes a kind of bat algorithm (BA), which can optimize the intrusion detection model of support vector machine (BA-SVM). In this algorithm, parameters of the SVM support vector machine are coded as individual bats first, and the detection rate of network intrusion is put as the parameter objective function. Then, the optimum parameter of support vector machine is found by simulating the bat flight. Finally, a network intrusion detection model is established based on optimal parameters, and simulation experiments are performed with KDD CUP99 dataset. The results show that this model could not only improve the detection rate of network intrusion, but also reduce the training time, and therefore improve the effect of network intrusion detection.
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Acknowledgments
This work was supported by the Inner Mongolia University for Nationalities Funds of China under Grant No. NMDYB15079.
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© 2016 Springer International Publishing Switzerland
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Cheng, C., Bao, L., Bao, C. (2016). Network Intrusion Detection with Bat Algorithm for Synchronization of Feature Selection and Support Vector Machines. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_46
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DOI: https://doi.org/10.1007/978-3-319-40663-3_46
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