Abstract
Recurrent Neural Networks (RNN’s) are deep neural (DL) networks that can be trained on large volume of databases and performed well on natural language processing, speech recognition, and other classification problems. Here in this paper exploring the application of recurrent neural network and its variants in network based attack detection systems. In this paper, we look at how recurrent neural networks and their variants can be used in network-based attack detection systems. This paper presents a detailed analysis of the state-of-the-art in attack detection system using recurrent neural networks, with a focus on results and discussion of key parameters such as false and true positives, alarm rates, accuracy, detection rate, and benchmarks for attack detection systems.
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Dixit, P., Silakari, S. (2022). Analysis of State-of-Art Attack Detection Methods Using Recurrent Neural Network. In: Dua, M., Jain, A.K., Yadav, A., Kumar, N., Siarry, P. (eds) Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-5747-4_68
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DOI: https://doi.org/10.1007/978-981-16-5747-4_68
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