An optimal feature subset selection technique to improve accounting information security for intrusion detection systems
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41870-024-01954-3.pdf
Reference20 articles.
1. Kamalov F, Moussa S, Zgheib R, Mashaal O (2020) Feature selection for intrusion detection systems. In: 2020 13th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, pp 265–269. https://doi.org/10.1109/ISCID51228.2020.00065
2. Desai R, Gopalakrishnan VT (2023) Network intrusion detection through machine learning with efficient feature selection. In: 2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India, pp 797–801. https://doi.org/10.1109/COMSNETS56262.2023.10041315
3. Azhagiri M, Rajesh A, Karthik S et al (2024) An intrusion detection system using ranked feature bagging. Int J Inf Technol 16:1213–1219. https://doi.org/10.1007/s41870-023-01621-z
4. Karthic S, Manoj Kumar S, Senthil Prakash PN (2022) Grey wolf based feature reduction for intrusion detection in WSN using LSTM. Int J Inf Technol 14:3719–3724. https://doi.org/10.1007/s41870-022-01015-7
5. Nguyen P-C, Nguyen Q-T, Le K-H (2021) An ensemble feature selection algorithm for machine learning based intrusion detection system. In: 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), Hanoi, Vietnam, pp 50–54. https://doi.org/10.1109/NICS54270.2021.9701577
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