Biblio

In this paper, we propose new types of cascading attacks against smart grid that use control command disaggregation and core smart grid services. Although there have been tremendous research efforts in injection attacks against the smart grid, to our knowledge most studies focus on false meter data injection, and false command and false feedback injection attacks have been scarcely investigated. In addition, control command disaggregation has not been addressed from a security point of view, in spite of the fact that it is becoming one of core concepts in the smart grid and hence analysing its security implications is crucial to the smart grid security. Our cascading attacks use false control command, false feedback or false meter data injection, and cascade the effects of such injections throughout the smart grid subsystems and components. Our analysis and evaluation results show that the proposed attacks can cause serious service disruptions in the smart grid. The evaluation has been performed on a widely used smart grid simulation platform.

State estimation plays a critically important role in ensuring the secure and reliable operation of the electric grid. Recent works have shown that the state estimation process is vulnerable to stealthy attacks where an adversary can alter certain measurements to corrupt the solution of the process, but evade the existing bad data detection algorithms and remain invisible to the system operator. Since the state estimation result is used to compute optimal power flow and perform contingency analysis, incorrect estimation can undermine economic and secure system operation. However, an adversary needs sufficient resources as well as necessary knowledge to achieve a desired attack outcome. The knowledge that is required to launch an attack mainly includes the measurements considered in state estimation, the connectivity among the buses, and the power line admittances. Uncertainty in information limits the potential attack space for an attacker. This advantage of uncertainty enables us to apply moving target defense (MTD) strategies for developing a proactive defense mechanism for state estimation.

In this paper, we propose an MTD mechanism for securing state estimation, which has several characteristics: (i) increase the knowledge uncertainty for attackers, (ii) reduce the window of attack opportunity, and (iii) increase the attack cost. In this mechanism, we apply controlled randomization on the power grid system properties, mainly on the set of measurements that are considered in state estimation, and the topology, especially the line admittances. We thoroughly analyze the performance of the proposed mechanism on the standard IEEE 14- and 30-bus test systems.

By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techniques such as denial-of-service (DoS) attack, random attack, and data-injection attack. In this paper, we present a mathematical model of the system to study these pitfalls and propose a robust security framework for the smart grid. Our framework adopts the Kalman filter to estimate the variables of a wide range of state processes in the model. The estimates from the Kalman filter and the system readings are then fed into the χ2-detector or the proposed Euclidean detector. The χ2-detector is a proven effective exploratory method used with the Kalman filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the χ2-detector is unable to detect the statistically derived false data-injection attack. To overcome this limitation, we prove that the Euclidean detector can effectively detect such a sophisticated injection attack.