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arXiv 2303.18136cs.CRcs.AIcs.LG
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针对智能电网故障预测系统的机器学习对抗攻击

Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids

Polytechnic University of Bari(巴里理工大学)

Carmelo Ardito, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fatemeh Nazary, Giovanni Servedio

AI总结:

本研究聚焦智能电网机器学习应用的安全挑战,证明智能电网所用深度神经网络易受对抗扰动,并通过故障定位与类型分类研究揭示现有ML算法在各类对抗攻击下的弱点。

摘要:

在智能电网中,故障检测任务因其经济影响和关键属性可能对社会产生重大影响。近年来,众多智能电网应用(如缺陷检测和负荷预测)已采用数据驱动方法。本研究旨在探究智能电网场景下机器学习(ML)应用安全相关的挑战。实际上,针对所有电网应用,这些数据驱动算法的鲁棒性和安全性尚未得到广泛研究。我们首先证明智能电网中使用的深度神经网络方法易受对抗扰动影响。随后,我们通过故障定位和类型分类研究,阐明了当前智能电网中的ML算法在各类对抗攻击下的弱点。

英文摘要:

In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with the security of machine learning (ML) applications in the smart grid scenario. Indeed, the robustness and security of these data-driven algorithms have not been extensively studied in relation to all power grid applications. We demonstrate first that the deep neural network method used in the smart grid is susceptible to adversarial perturbation. Then, we highlight how studies on fault localization and type classification illustrate the weaknesses of present ML algorithms in smart grids to various adversarial attacks

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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