Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks
This work addresses security risks in 5G networks by demonstrating how adversarial attacks degrade model performance, but it is incremental as it applies known methods to a specific domain.
The study analyzed the vulnerability of machine learning regression models to adversarial attacks using 5G wireless network data, showing that an FGSM attack increased MSE by 33% and decreased R2 by 10% on average, while a LightGBM classifier detected adversarial anomalies with 98% accuracy.
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% and a decrease in the R2 indicator by 10% on average. The LightGBM binary classifier effectively identifies data with adversarial anomalies with 98% accuracy. Regression 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