Abstract-State estimators in power systems are currently used to, for example, detect faulty equipment and to route power flows. It is believed that state estimators will also play an increasingly important role in future smart power grids, as a tool to optimally and more dynamically route power flows. Therefore security of the estimator becomes an important issue. The estimators are currently located in control centers, and large numbers of measurements are sent over unencrypted communication channels to the centers. We here study stealthy false-data attacks against these estimators. We define a security measure tailored to quantify how hard attacks are to perform, and describe an efficient algorithm to compute it. Since there are so many measurement devices in these systems, it is not reasonable to assume that all devices can be made encrypted overnight in the future. Therefore we propose two algorithms to place encrypted devices in the system such as to maximize their utility in terms of increased system security. We illustrate the effectiveness of our algorithms on two IEEE benchmark power networks under two attack and protection cost models.

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Abstract-State estimation plays an essential role in the monitoring and supervision of power systems. In today's power systems state estimation is typically done in a centralized or in a hierarchical way, but as power systems will be increasingly interconnected in the future smart grid, distributed state estimation will become an important alternative to centralized and hierarchical solutions. As the future smart grid may rely on distributed state estimation, it is essential to understand the potential vulnerabilities that distributed state estimation may have. In this paper, we show that an attacker that compromises the communication infrastructure of a single control center in an interconnected power system can successfully perform a denial of service attack against state-of-the-art distributed state estimation, and consequently it can blind the system operators of every region. As a solution to mitigate such a denial of service attack, we propose a fully distributed algorithm for attack detection. Furthermore, we propose a fully distributed algorithm that identifies the most likely attack location based on the individual regions' beliefs about the attack location, isolates the identified region, and then reruns the distributed state estimation. We validate the proposed algorithms on the IEEE 118 bus benchmark power system.

The electrical power network is a critical infrastructure in today's society, so its safe and reliable operation is of major concern. State estimators are commonly used in power networks, for example, to detect faulty equipment and to optimally route power flows. The estimators are often located in control centers, to which large numbers of measurements are sent over unencrypted communication channels. Therefore cyber security for state estimators becomes an important issue. In this paper we analyze the cyber security of state estimators in supervisory control and data acquisition (SCADA) for energy management systems (EMS) operating the power network. Current EMS state estimation algorithms have bad data detection (BDD) schemes to detect outliers in the measurement data. Such schemes are based on high measurement redundancy. Although these methods may detect a set of basic cyber attacks, they may fail in the presence of an intelligent attacker. We explore the latter by considering scenarios where stealthy deception attacks are performed by sending false information to the control center. We begin by presenting a recent framework that characterizes the attack as an optimization problem with the objective specified through a security metric and constraints corresponding to the attack cost. The framework is used to conduct realistic experiments on a state-of-the-art SCADA EMS software for a power network example with 14 substations, 27 buses, and 40 branches. The results indicate how state estimators for power networks can be made more resilient to cyber security attacks.

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