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Using Machine Learning Algorithms and Honeypot Systems to Detect Adversarial Attacks on Intrusion Detection Systems

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Abstract

This paper presents adversarial attacks on machine learning (ML) algorithms in intrusion detection systems (IDSs). Some examples of existing IDSs are examined. The existing approaches for detecting these attacks are considered. Requirements are developed to increase the stability of ML algorithms. Two approaches to detect adversarial attacks on ML algorithms are proposed, the first of which is based on a multiclass classifier and a Honeypot system, and the second approach uses a combination of a multiclass and binary classifier. The proposed approaches can be used in further research aimed at detecting adversarial attacks on ML algorithms.

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This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    P. E. Yugai & D. A. Moskvin

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  1. P. E. Yugai
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  2. D. A. Moskvin
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Yugai, P.E., Moskvin, D.A. Using Machine Learning Algorithms and Honeypot Systems to Detect Adversarial Attacks on Intrusion Detection Systems. Aut. Control Comp. Sci. 58, 1226–1233 (2024). https://doi.org/10.3103/S014641162470086X

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