Abstract
Intrusion detection system (IDS) is used to monitor the intrusions or suspicious actions over the network traffic data or in the computer system. In this paper, we propose an IDS for identifying the intrusion over the network traffic data. As the network traffic data are continuous in nature, we used the Gaussian Naïve Bayes classification approach with the IDS to deduct the intrusions. We used the Kyoto dataset to evaluate the performance of the proposed approach. The results show that the proposed approach have better accuracy of intrusion detection, high intrusion detection rate, and low false alarm rate than the existing approaches.
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Meerja, A.J., Ashu, A., Rajani Kanth, A. (2021). Gaussian Naïve Bayes Based Intrusion Detection System. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_16
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DOI: https://doi.org/10.1007/978-3-030-49345-5_16
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