Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning

2020/01/07 by Zefan Tang, Jieying Jiao, Tang, Zefan +9
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Electricity Theft Detection Techniques #FOS: Electrical engineering #Smart Grid Security and Resilience #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2001.02289

openalex publication_date 2020/01/07 · openalex created_date 2020/01/23 · openalex updated_date 2026/07/28

Abstract

In the face of an increasingly broad cyberattack surface, cyberattack-resilient load forecasting for electric utilities is both more necessary and more challenging than ever. In this paper, we propose an adversarial machine learning (AML) approach, which can respond to a wide range of attack behaviors without detecting outliers. It strikes a balance between enhancing a system's robustness against cyberattacks and maintaining a reasonable degree of forecasting accuracy when there is no attack. Attack models and configurations for the adversarial training were selected and evaluated to achieve the desired level of performance in a simulation study. The results validate the effectiveness and excellent performance of the proposed method.

Citations

• Cyber–Physical System Security for the Electric Power Grid
• Short-term load forecasting
• Global Energy Forecasting Competition 2012
• Curriculum Adversarial Training

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