SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection
This work addresses a critical security problem for utility companies by revealing vulnerabilities in existing ML-based energy theft detection methods, which is incremental as it builds on known adversarial attack techniques applied to a specific domain.
The paper tackled the vulnerability of machine learning-based energy theft detection systems by demonstrating that they are highly susceptible to adversarial attacks, showing that their approach can significantly decrease detection accuracy, even in black-box scenarios.
Energy theft causes large economic losses to utility companies around the world. In recent years, energy theft detection approaches based on machine learning (ML) techniques, especially neural networks, become popular in the research literature and achieve state-of-the-art detection performance. However, in this work, we demonstrate that the well-perform ML models for energy theft detection are highly vulnerable to adversarial attacks. In particular, we design an adversarial measurement generation algorithm that enables the attacker to report extremely low power consumption measurements to the utilities while bypassing the ML energy theft detection. We evaluate our approach with three kinds of neural networks based on a real-world smart meter dataset. The evaluation result demonstrates that our approach can significantly decrease the ML models' detection accuracy, even for black-box attackers.