Abstract
Cyber-attacks have increased in recent years; however, the classic Network Intrusion Detection System based on feature selection by filtering has significant disadvantages that make it difficult to stop new attacks promptly. An anomaly-based hybrid deep learning system for detecting network intrusions is done by using various neural networks such as Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). Extreme Gradient Boosting with Shapley Additive Explanations (SHAP) values-based supervised machine learning feature selection methods have been used to optimally select the number of features. Optimization techniques such as Adam and Root Mean Square Propagation (RMSPROP) are used to further enhance the performance of the deep learning classifier. Finally, the mechanism is examined using simulation characteristics including precision, accuracy, recall, and F1-score. The model is tested on two benchmark datasets, CICIDS2017 and UNSW-NB15. This sort of research aids in the identification of the optimal algorithm for predicting future cyber-attacks, especially in the vulnerable public healthcare industry.
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OmaMageswari, M., Peroumal, V., Ghosh, R., Goswami, D. (2023). Gated Recurrent Unit and Long Short-Term Memory Based Hybrid Intrusion Detection System. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_53
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