Abstract
The purpose of this paper is to determine the key characteristics required for intrusion detection model construction to achieve the greatest accuracy in detection. In this paper, Two-Class Neural Network, Two-Class Locally-Deep Support Vector Machine, and Two-Class Averaged Perceptron are selected. To test the accuracy of intrusion detection. This paper uses the NSL-KDD data set to discuss the accuracy of binary classification in classification, uses permutation feature importance to rank features, and according to the analysis, results show that (“Service”,“dst_host_rerror_rate”,“Flag”,) features are the most important.
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Li, J., Zheng, W., Chang, K.C., Wang, S., Luo, Y. (2021). Critical Features Selection of Training Model for Intrusion Detection System. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_34
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DOI: https://doi.org/10.1007/978-3-030-69717-4_34
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