Abstract
Intrusion detection is very essential in the field of information security. The cornerstone of an Intrusion Detection System (IDS) is to accurately identify different attacks in a network. In this paper, a deep learning system to detect intrusions is proposed. The existing recurrent neural network (RNN-IDS) based IDS is expanded to include Long Short term memory (LSTM) and the results are compared. The binary classification performance of the RNN-IDS is tested with various learning rates and using different number of hidden nodes. The results show that by integrating LSTM with RNN-IDS, the accuracy of intrusion prediction has improved against the benchmark dataset.
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Rajkumar, N., D’Souza, A., Alex, S., Kathrine, G.J.W. (2019). Long Short-Term Memory-Based Recurrent Neural Network Approach for Intrusion Detection. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_81
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DOI: https://doi.org/10.1007/978-3-030-00665-5_81
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