Abstract
Intrusion Detection is crucial in cybersecurity. So is the ability to identify the myriad of attacks. Artificial Neural Networks are an established and proven method of accurate classification. There are approaches to make ANN models faster by applying Principal Component Analysis as a feature extractor. However, ANNs are extremely versatile, a wide range of setups can achieve significantly different classification results. The main contribution of this paper is the evaluation of the way the hyperparameters can influence the final classification result. In this paper, a wide range of ANN setups is put to comparison, and the finest arrangement achieves the multi-class classification accuracy of 99.909% on an established benchmark dataset.
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Pawlicki, M., Kozik, R., Choraś, M. (2019). Artificial Neural Network Hyperparameter Optimisation for Network Intrusion Detection. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_72
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DOI: https://doi.org/10.1007/978-3-030-26763-6_72
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