KDSR: Hybrid Machine-Learning Solution for Intrusion Detection in Fog Computing Environment
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3601-0_28
Reference11 articles.
1. FuatTürk (2023) Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms. J 12(2):465–477
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3. Bhardwaj S, Dave M (2023) Enhanced neural network-based attack investigation framework for network forensics: identification, detection, and analysis of the attack. Comput Secur 135:103521. https://doi.org/10.1016/j.cose.2023.103521
4. Thockchom N, Singh MM, Nandi U (2023) A novel ensemble learning-based model for network intrusion detection. Complex Intell Syst 9(5):5693–5714. https://doi.org/10.1007/s40747-023-01013-7
5. Vishwakarma M, Kesswani N (2023) A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection. Decision Anal J 7:100233. https://doi.org/10.1016/j.dajour.2023.100233
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