RISK ASSESSMENT OF MALICIOUS ATTACKS AGAINST POWER SYSTEMS

The new scenarios of malicious attack prompt for their deeper consideration and mainly when critical systems are at stake. In this framework, infrastructural systems, including power systems, represent a possible target due to the huge impact they can have on society. Malicious attacks are different in their nature from other more traditional cause of threats to power system, since they embed a strategic interaction between the attacker and the defender (characteristics that cannot be found in natural events or systemic failures). This difference has not been systematically analyzed by the existent literature. In this respect, new approaches and tools are needed. This paper presents a mixed-strategy game-theory model able to capture the strategic interactions between malicious agents that may be willing to attack power systems and the system operators, with its related bodies, that are in charge of defending them. At the game equilibrium, the different strategies of the two players, in terms of attacking/protecting the critical elements of the systems, can be obtained. The information about the attack probability to various elements can be used to assess the risk associated with each of them, and the efficiency of defense resource allocation is evidenced in terms of the corresponding risk. Reference defense plans related to the online defense action and the defense action with a time delay can be obtained according to their respective various time constraints. Moreover, risk sensitivity to the defense/attack-resource variation is also analyzed. The model is applied to a standard IEEE RTS-96 test system for illustrative purpose and, on the basis of that system, some peculiar aspects of the malicious attacks are pointed ou

Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise

In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We show that the conventional approach with the assumption of Gaussian noise is a special case of the proposed method, and thus the new approach has more applicability. {The proposed detector is also tested on an independent component analysis (ICA) based unobservable false data attack scheme that utilizes similar assumptions of sample observation.} We evaluate the performance of the proposed state estimator and attack detector on the IEEE 30-bus power system with comparison to conventional Gaussian noise based detector. The superior performance of {both observable and unobservable false data attacks} demonstrates the effectiveness of the proposed approach and indicates a wide application on the power signal processing.Comment: 8 pages, 4 figures in IEEE Conference on Communications and Network Security (CNS) 201