Biblio

Smart grids require communication networks for supervision functions and control operations. With this they become attractive targets for attackers. In newer power grids, State Estimation (SE) is often performed based on Kalman Filters (KFs) to deal with noisy measurement data and detect Bad Data (BD) due to failures in the measurement system. Nevertheless, in a setting where attackers can gain access to modify sensor data, they can exploit the fact that SE is used to process the data. In this paper, we show how an attacker can modify Phasor Measurement Unit (PMU) sensor data in a way that it remains undetected in the state estimation process. We show how anomaly detection methods based on innovation gain fail if an attacker is aware of the state estimation and uses the right strategy to circumvent detection.

Wide Area Monitoring Systems (WAMSs) provide an essential building block for Smart Grid supervision and control. Distributed Phasor Measurement Units (PMUs) allow accurate clock-synchronized measurements of voltage and current phasors (amplitudes, phase angles) and frequencies. The sensor data from PMUs provide situational awareness in the grid, and are used as input for control decisions. A modification of sensor data can severely impact grid stability, overall power supply, and physical devices. Since power grids are critical infrastructures, WAMSs are tempting targets for all kinds of attackers, including well-organized and motivated adversaries such as terrorist groups or adversarial nation states. Such groups possess sufficient resources to launch sophisticated attacks. In this paper, we provide an in-depth analysis of attack possibilities on WAMSs. We model the dependencies and building blocks of Advanced Persistent Threats (APTs) on WAMSs using attack trees. We consider the whole WAMS infrastructure, including aggregation and data collection points, such as Phasor Data Concentrators (PDCs), classical IT components, and clock synchronization. Since Smart Grids are cyber-physical systems, we consider physical perturbations, in addition to cyber attacks in our models. The models provide valuable information about the chain of cyber or physical attack steps that can be combined to build a sophisticated attack for reaching a higher goal. They assist in the assessment of physical and cyber vulnerabilities, and provide strategic guidance for the deployment of suitable countermeasures.