Research on Cyberspace Intrusion Detection Model for Wireless Agile Network Planning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9021-4_13
Reference15 articles.
1. Roy, S.S., Mallik, A., Gulati, R., et al.: A deep learning based artificial neural network approach for intrusion detection. In: International Conference on Mathematic, and Computing, pp. 44–53. Springer, Singapore (2017)
2. Shum, J., Malki, H.A.: Network intrusion detection system using neural networks. In: Fourth International Conference on Natural Computation, pp. 242–246 (2008)
3. Engen, V., Vincent, J., Phalp, K.: Enhancing network based intrusion detection for imbalanced data. Int. J. Knowl. Based Intell. Eng. Syst. 12(5,6) (2009)
4. Lin, W.H., Lin, H.C., Wang, P., et al.: Using convolutional neural networks to network intrusion detection for cyber threats. In: 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 1107–1110. IEEE, New York (2018)
5. Blanco, R., Malagon, P., Cilla, J.J., et al.: Multiclass network attack classifier using CNN tuned with genetic algorithms. In: 2018 the 28th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS), pp. 177–182. IEEE, New York (2018)
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