A context-aware robust intrusion detection system: a reinforcement learning-based approach-Reference-Cited by-同舟云学术

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A context-aware robust intrusion detection system: a reinforcement learning-based approach

Published:2019-12-03 Issue:6 Volume:19 Page:657-678
ISSN:1615-5262
Container-title:International Journal of Information Security
language:en
Short-container-title:Int. J. Inf. Secur.

Author:

Sethi Kamalakanta,Sai Rupesh E.,Kumar Rahul,Bera Padmalochan,Venu Madhav Y.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10207-019-00482-7.pdf

Reference66 articles.

1. Mohamed, A.B., Idris, N.B., Shanmugum, B.: A brief introduction to intrusion detection system. In: International Conference on Intelligent Robotics, Automation, and Manufacturing, pp. 263–271. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-35197-6_29

2. Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153–1176 (2016). https://doi.org/10.1109/COMST.2015.2494502

3. Kuang, F., Xu, W., Zhang, S.: A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl. Soft Comput. 18, 178–184 (2014)

4. Reddy, R.R., Ramadevi, Y., Sunitha, K.V.N.: Effective discriminant function for intrusion detection using SVM. In: Proceedings of International Conference on Advance in Computing, Communication and Information (ICACCI), pp. 1148–1153 (2016)

5. Li, W., Yi, P., Wu, Y., Pan, L., Li, J.: A new intrusion detection system based on KNN classification algorithm in wireless sensor network. J. Electron. Comput. Eng. 2014, 240217 (2014)

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