Correlative monitoring for detection of false data injection attacks in smart grids

The overarching objective of the modernized electric grid, the smart grid, is to integrate two-way communication technologies across power generation, transmission and distribution to deliver electricity efficiently, securely and cost-effectively. However, real-time messaging exposes the entire grid to security threats ranging from attacks that disable information exchange between smart meters and data fusion centers to spurious payload content that would lead to incorrect assessment of actual demand. Such nefarious activities can compromise grid stability and efficiency. Hence, it is important to ensure secure communications and quickly detect malicious activity; this article proposes a framework for detection of false data injection attacks in smart grids. We present a measurement-based situation awareness framework that combines evidence from sensors at home-area networks, and aims to infer anomalies that signify a coordinated, well-orchestrated attack on residential smart meters at increasing spatial scales. By leveraging multi-view sensor readings, we present a Bayesian-based correlative monitoring approach that quickly detects power shifts to anomalous regimes. We evaluate our algorithms using real-world power traces.