Email this article Machine learning-based defense against adversarial attacks in intrusion detection systems
- Authors: Niang P.M.1, Sidorenko V.G.1
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Affiliations:
- Russian University of Transport RUT (MIIT)
- Issue: Vol 31, No 12 (2025)
- Pages: 637-648
- Section: Information security
- Published: 15.12.2025
- URL: https://journals.eco-vector.com/1684-6400/article/view/702053
- DOI: https://doi.org/10.17587/it.31.637-648
- ID: 702053
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Abstract
In this paper, common types of adversarial attacks (DTA, FGSM, and BIM) are used to generate adversarial samples to test the vulnerability of IDS using the UNSW-NB15 dataset. Then, basic defense mechanisms are developed, including adversarial pattern detection and filtering. Experiments are conducted on Random Forest (RF) and Logistic Regression (LR) machine learning classifier.
About the authors
P. M. Niang
Russian University of Transport RUT (MIIT)
Author for correspondence.
Email: malickdiarra30@gmail.com
Graduate Student, Department of Information Management and Protection
Russian Federation, Moscow, 127994V. G. Sidorenko
Russian University of Transport RUT (MIIT)
Email: valenfalk@mail.ru
Dr. of Tech. Sc., Professor, Department of Information Management and Protection
Russian Federation, Moscow, 127994References
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