Local Cyber-Physical Attack for Masking Line Outage and Topology Attack in Smart Grid

Malicious attacks in the power system can eventually result in a large-scale cascade failure if not attended on time. These attacks, which are traditionally classified into \emph{physical} and \emph{cyber attacks}, can be avoided by using the latest and advanced detection mechanisms. However, a new threat called \emph{cyber-physical attacks} which jointly target both the physical and cyber layers of the system to interfere the operations of the power grid is more malicious as compared with the traditional attacks. In this paper, we propose a new cyber-physical attack strategy where the transmission line is first physically disconnected, and then the line-outage event is masked, such that the control center is misled into detecting as an obvious line outage at a different position in the local area of the power system. Therefore, the topology information in the control center is interfered by our attack. We also propose a novel procedure for selecting vulnerable lines, and analyze the observability of our proposed framework. Our proposed method can effectively and continuously deceive the control center into detecting fake line-outage positions, and thereby increase the chance of cascade failure because the attention is given to the fake outage. The simulation results validate the efficiency of our proposed attack strategy.Comment: accepted by IEEE Transactions on Smart Grid. arXiv admin note: text overlap with arXiv:1708.0320

Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements

The false data injection (FDI) attack cannot be detected by the traditional anomaly detection techniques used in the energy system state estimators. In this paper, we demonstrate how FDI attacks can be constructed blindly, i.e., without system knowledge, including topological connectivity and line reactance information. Our analysis reveals that existing FDI attacks become detectable (consequently unsuccessful) by the state estimator if the data contains grossly corrupted measurements such as device malfunction and communication errors. The proposed sparse optimization based stealthy attacks construction strategy overcomes this limitation by separating the gross errors from the measurement matrix. Extensive theoretical modeling and experimental evaluation show that the proposed technique performs more stealthily (has less relative error) and efficiently (fast enough to maintain time requirement) compared to other methods on IEEE benchmark test systems.Comment: Keywords: Smart grid, False data injection, Blind attack, Principal component analysis (PCA), Journal of Computer and System Sciences, Elsevier, 201

False Data Injection Attacks on Phasor Measurements That Bypass Low-rank Decomposition

This paper studies the vulnerability of phasor measurement units (PMUs) to false data injection (FDI) attacks. Prior work demonstrated that unobservable FDI attacks that can bypass traditional bad data detectors based on measurement residuals can be identified by detector based on low-rank decomposition (LD). In this work, a class of more sophisticated FDI attacks that captures the temporal correlation of PMU data is introduced. Such attacks are designed with a convex optimization problem and can always bypass the LD detector. The vulnerability of this attack model is illustrated on both the IEEE 24-bus RTS and the IEEE 118-bus systems.Comment: 6 pages, 4 figures, submitted to 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm

Topology Detection in Microgrids with Micro-Synchrophasors

Network topology in distribution networks is often unknown, because most switches are not equipped with measurement devices and communication links. However, knowledge about the actual topology is critical for safe and reliable grid operation. This paper proposes a voting-based topology detection method based on micro-synchrophasor measurements. The minimal difference between measured and calculated voltage angle or voltage magnitude, respectively, indicates the actual topology. Micro-synchrophasors or micro-Phasor Measurement Units ({\mu}PMU) are high-precision devices that can measure voltage angle differences on the order of ten millidegrees. This accuracy is important for distribution networks due to the smaller angle differences as compared to transmission networks. For this paper, a microgrid test bed is implemented in MATLAB with simulated measurements from {\mu}PMUs as well as SCADA measurement devices. The results show that topologies can be detected with high accuracy. Additionally, topology detection by voltage angle shows better results than detection by voltage magnitude.Comment: 5 Pages, PESGM2015, Denver, C