Analysis of State-of-Art Attack Detection Methods Using Recurrent Neural Network

Author:

Dixit Priyanka,Silakari Sanjay

Publisher

Springer Singapore

Reference16 articles.

1. Radford, B.J., Apolonio, L.M., Trias, A.J., Simpson, J.A.: Network traffic anomaly detection using recurrent neural networks. In: Proceedings of the 2017 National Symposium on Sensor Data and Fusion (2017)

2. Dixit, P., Silakari, S.: Deep learning algorithms for cybersecurity applications: a technological and status review. Comput. Sci. Rev. 39 (2021)

3. Hodo, E., Bellekens, X., Hamilton, A., Tachtatzis, C., Atkinson, R.: Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey. Jan (2017). (Eprint) arxiv:1701.02145

4. Ferraga, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 20 (2020)

5. Fu, Y., Lou, F., Meng, F., Tian, Z., Zhang, H., Jiang, F.: An intelligent network attack detection method based on RNN. In: 2018 IEEE Third International Conference on Data Science in Cyberspace

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