Abstract
Network intrusion detection has become a hot topic in cyber security research due to better advancements in deep learning. The research is lacking an objective comparison of the various deep learning models in a controlled setting, notably on recent intrusion detection datasets, despite the fact that several outstanding studies address the growing body of research on the subject. In this paper, a network intrusion scheme is developed as a solution of the discussed issue. The four different models are build and are experimented with NSL-KDD dataset. These deep learning models are LightGBM, XGBoost, LSTM, and decision tree. For the validation of the proposed scheme, the proposed scheme is also experimented with UNSW-NB15 dataset and CIC-IDS2017. However, the experiments concluded that the proposed scheme outperforms and the discussion is also illustrated.
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Sama, L., Wang, H., Watters, P. (2022). Enhancing System Security by Intrusion Detection Using Deep Learning. In: Hua, W., Wang, H., Li, L. (eds) Databases Theory and Applications. ADC 2022. Lecture Notes in Computer Science, vol 13459. Springer, Cham. https://doi.org/10.1007/978-3-031-15512-3_14
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