Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks

Authors: Leonid Legashev, Artur Zhigalov, Denis Parfenov
Published: 2025-05-01

Source: https://arxiv.org/abs/2505.00487

PDF: https://arxiv.org/pdf/2505.00487

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ポイズニング 攻撃タイプ バックドア攻撃

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Abstract

This article describes the process of creating a script and conducting an analytical study of a dataset using the DeepMIMO emulator. An advertorial attack was carried out using the FGSM method to maximize the gradient. A comparison is made of the effectiveness of binary classifiers in the task of detecting distorted data. The dynamics of changes in the quality indicators of the regression model were analyzed in conditions without adversarial attacks, during an adversarial attack and when the distorted data was isolated. It is shown that an adversarial FGSM attack with gradient maximization leads to an increase in the value of the MSE metric by 33 indicator by 10 identifies data with adversarial anomalies with 98 machine learning models are susceptible to adversarial attacks, but rapid analysis of network traffic and data transmitted over the network makes it possible to identify malicious activity