Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Abstract

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.