研究5G网络中回归模型对抗攻击漏洞，发现攻击使误差升33%、准确率降10%
Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks
- 用FGSM方法生成对抗样本，放大梯度增强攻击效果
- 攻击使MSE上升33%，R2下降10%，模型性能显著退化
- 轻量级分类器可98%准确识别异常数据，适合实时防御
本文通过DeepMIMO模拟器构建脚本并分析数据集，采用FGSM方法实施对抗攻击以最大化梯度。对比了二分类器在检测异常数据上的有效性。分析了回归模型在无攻击、受攻击及数据隔离三种状态下的质量指标变化。结果表明，采用梯度最大化的对抗性FGSM攻击使均方误差（MSE）平均增加33%，决定系数（R²）下降10%。使用LightGBM的二分类器能以98%的准确率有效识别含对抗异常的数据。研究表明，回归机器学习模型易受对抗攻击影响，但通过快速分析网络流量与传输数据，可实现恶意行为的及时识别。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。