With the advent of the advanced metering infrastructure, electricity usage data is being continuously generated at large volumes by smart meters vastly deployed across the modern power grid. Electric power utility companies and third party entities such as smart home management solution providers gain significant insights into these datasets via machine learning (ML) models. These are then utilized to perform active/passive power demand management that fosters economical and sustainable electricity usage. Although ML models are powerful, these remain vulnerable to adversarial attacks. A novel stealthy black-box attack construction model is proposed that targets deep learning models utilized to perform non-intrusive load monitoring based on smart meter data. These attacks are practical as there is no assumption of the knowledge of training data, internal parameters, and architecture of the targeted ML model. The profound impact of the proposed stealthy attack constructions on energy analytics and decision-making processes is shown through comprehensive theoretical, practical, and comparative analysis. This work sheds light on vulnerabilities of ML models in the smart grid context and provides valuable insights for securely accommodating increasing prevalence of artificial intelligence in the modern power grid.

中文翻译：

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对深度学习非侵入式负载监控模型的隐形黑盒攻击

随着先进计量基础设施的出现，现代电网中广泛部署的智能电表不断产生大量用电量数据。电力公用事业公司和第三方实体（例如智能家居管理解决方案提供商）通过机器学习 (ML) 模型获得对这些数据集的重要见解。然后利用这些来执行主动/被动电力需求管理，以促进经济和可持续的电力使用。尽管 ML 模型很强大，但它们仍然容易受到对抗性攻击。提出了一种新颖的隐身黑盒攻击构建模型，该模型针对用于基于智能电表数据执行非侵入式负载监控的深度学习模型。这些攻击是实用的，因为没有对训练数据知识的假设，目标 ML 模型的内部参数和架构。通过全面的理论、实践和比较分析，显示了拟议的隐身攻击结构对能源分析和决策过程的深远影响。这项工作揭示了智能电网环境中 ML 模型的漏洞，并为安全地适应现代电网中日益流行的人工智能提供了宝贵的见解。