Drift-aware Unsupervised Detection of Stealthy FDIA Towards Energy Market

The energy market has undergone significant evolution, driven by modern energy systems. Nonetheless, the underlying infrastructure remains vulnerable to stealthy attack vectors and exploits, particularly False Data Injection Attacks (FDIAs). Profit-oriented FDIAs strategically manipulate Locational Marginal Prices (LMPs) to achieve long-term financial gains. The stealthy nature of such manipulations makes them difficult to distinguish from normal price fluctuations, posing severe threats to market stability. In parallel, the dynamic nature of energy markets shaped by seasonal variations, demand shifts, and generation changes adds complexity to the detection of these attacks. Given the aforementioned constraints, we introduce a novel market-level unsupervised model designed to detect stealthy LMP manipulations. We thus leverage non-parametric change-point detection principles to identify anomalous pricing behaviors without prior knowledge of attack patterns. In order to enhance robustness and higher confidence on our anomaly diagnosis, we propose a drift adaptive mechanism by adapting to evolving data distributions. Our extensive experiments on synthetic and real-world LMP datasets, showcase the effectiveness of our approach in reducing false alarm rates compared to state-of-the-art methods. Hence, we justify through our experimental outcomes the potential of adaptive unsupervised models in safeguarding modern energy markets against sophisticated and financially motivated cyber threat vectors.