A Review Analysis Investigating the Efficacy of Machine Learning in Intrusion Detection-Reference-Cited by-同舟云学术

A Review Analysis Investigating the Efficacy of Machine Learning in Intrusion Detection

Published:2022 Issue: Volume: Page:266-274
ISSN:2367-3370
Container-title:Artificial Intelligence for Sustainable Finance and Sustainable Technology
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Author:

ALshammari Majdah,Mezher Mohammad A.^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-93464-4_27

Reference24 articles.

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2. Zhang, J., Zulkernine, M., Haque, A.: Random-Forests-based network intrusion. IEEE Trans. Syst. Man Cybern.—Part C: Appl. Rev. 38, 649–659 (2008)

3. Scarfone, K., Mell, P.: Guide to Intrusion Detection and Prevention Systems (IDPS). Department of Commerce, National Institute of Standards and Technology, Gaithersburg (2007)

4. Hansen, L., Salomon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12, 993–1001 (1990)

5. Gang, W., Jinxing, H., Jian, M.: A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37(2011), 6225–6232 (2014)

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