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Abstract <p>A study of adversarial attacks on classical machine learning (ML) algorithms in the context of network threat detection is presented. An overview of ML models that are used to perform various tasks in computer network security systems is presented. A formal description of the threat model is provided, as well as a classification of adversarial attacks. The network traffic of the WEB-IDS23 dataset was classified using classical machine learning models: k-nearest neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). Adversarial attacks such as the fast gradient sign method (FGSM), projected gradient descent, Carlini &amp; Wagner (C &amp; W), and DeepFool are implemented on these ML algorithms. The impact of the adversarial attacks implemented on listed classical machine learning algorithms is analyzed.</p>

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Study of Adversarial Attacks on Classical Machine Learning Models in the Context of Network Threat Detection

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • P. E. Yugai

摘要

Abstract

A study of adversarial attacks on classical machine learning (ML) algorithms in the context of network threat detection is presented. An overview of ML models that are used to perform various tasks in computer network security systems is presented. A formal description of the threat model is provided, as well as a classification of adversarial attacks. The network traffic of the WEB-IDS23 dataset was classified using classical machine learning models: k-nearest neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). Adversarial attacks such as the fast gradient sign method (FGSM), projected gradient descent, Carlini & Wagner (C & W), and DeepFool are implemented on these ML algorithms. The impact of the adversarial attacks implemented on listed classical machine learning algorithms is analyzed.