Bibliographical review on cyber attacks from a control oriented perspective

Maintaining an accurate power system model is crucial for a variety of applications such as electricity markets. In this paper, the cyber security issue induced by critical parameters in electricity market operations is investigated. Critical parameters are a set of model parameters the errors of which cannot be detected due to the lack of local measurements. In this paper, it is first proven that the criticality property of parameters is independent from system operating points, i.e., the maliciously injected errors will always remain undetectable. Then, the cyber attack against critical parameters is formulated as a bilevel programming problem in search of the optimal attack strategy or the worst situation faced by the system operators. An effective algorithm for solving this problem is also provided. Simulation results verify that by injecting undetectable errors into critical model parameters, the locational marginal prices in electricity markets can be significantly distorted, which leads to financial benefits for the attackers.

Power System Parameter Attack for Financial Profits in Electricity Markets

As a typical representative of the so-called cyber-physical system, smart grid reveals its high efficiency, robustness and reliability compared with conventional power grid. However, due to the deep integration of electrical components and computinginformation in cyber space, smart grid is vulnerable to malicious attacks, especially for a type of attacks named false data injection attacks (FDIAs). FDIAs are capable of tampering meter measurements and affecting the results of state estimation stealthily, which severely threat the security of smart grid. Due to the significantinfluence of FDIAs on smart grid, the research related to FDIAs has received considerable attention over the past decade. This paper aims to summarize recent advances in FDIAs against smart grid state estimation, especially from the aspects of background materials, construction methods, detection and defense strategies. Moreover, future research directions are discussed and outlined by analyzing existing results. It is expected that through the review of FDIAs, the vulnerabilities of smart grid to malicious attacks can be further revealed and more attention can be devoted to the detection and defense of cyber-physical attacks against smart grid.

False data injection attacks against smart gird state estimation: Construction, detection and defense

False Data Injection Attack (FDIA) is one of the most dangerous cyber attacks against smart power grids, as it could cause severe physical and economic damage. In this paper, we review and compare previous surveys on FDIA, which mostly focus only on the state estimation component. Differently, our survey describes the FDIAs that target the different components of the on-line power system security. It also provides two novel attack classifications. The first classification categorizes the different FDIAs with respect to three levels: targeted systems at the first level, targeted subsystems at the second level, and the attacks targeting the subsystems at the third level. The second classification considers two criteria: targeted sub system and the impact of the attack, which can be physical and/or economic. The countermeasures are classified according to two dimensions: (i) the targeted subsystem and (ii) the class of countermeasure: preventive or detective. Both preventive and detective classes are further categorized according to different approaches. In addition, the countermeasures are presented along with their performance results. Finally, open issues are identified, and future research directions are recommended.

Survey of false data injection in smart power grid: Attacks, countermeasures and challenges

Successful detection of false data injection attacks (FDIAs) and removal of state bias due to FDIAs are essential for ensuring secure power grids operation and control. This article first extends the approximate dc model of FDIA to a more general ac model that can handle both traditional and synchronized measurements. To automatically filter out the established FDIAs, we propose a state reconstruction scheme consisting of a contaminated state separation method, an enhanced bad data identification approach and a state recovery algorithm. In this scheme, a classifier is developed by aggregating a series of extreme learning machines (ELMs) to detect anomaly states caused by FDIAs. Gaussian random distribution and Latin hypercube sampling are adopted to initialize the input weights of base ELMs, which can provide more diversities to enhance the ensemble performance. Then, to identify the exact locations of the compromised measurements, a state forecasting-based bad data identification approach is proposed by exploiting the consistency between the forecasted and the received measurements. Finally, an effective state recovery algorithm applies quasi-Newton method and Armijo line search to address the possible system unobservable problem due to the removal of attacked measurements. Numerical tests on serval IEEE standard test systems verify the efficiency of the proposed FDIA model and state reconstruction scheme.

Extreme Learning Machine-Based State Reconstruction for Automatic Attack Filtering in Cyber Physical Power System

Electricity markets consist of multiple look-ahead and real-time spot markets, across which energy price is generally volatile. Moreover, dispatch and pricing decisions in the real-time markets strongly hinge on the quality of the real-time state estimates, which are formed dynamically in order to delineate real-time information about operational state of the grid. Adversaries can leverage price volatility in conjunction with the dependence of the real-time markets on the state estimates in order to carry out profitable financial misconduct, e.g., via virtual bidding on the locational marginal prices. When the adversaries are omniscient (i.e., have full and instantaneous access to grid topology and dynamics), the attack strategies for maximizing financial profits are studied extensively in the existing literature. This paper focuses on limited adversaries who have only partial and imperfect information about the grid, and offers a framework for analyzing the attack strategies for limited adversaries and the associated confidence about profitable attacks. Specifically, adversaries’ information is considered to be within a measure of bounded error, and attack strategies are designed such that the adversaries are guaranteed a certain level of confidence to gain profit. Designing such attacks is investigated analytically and examined in the IEEE 14- and 118-bus systems.

Mateo R. Mengis

Papers

Data Injection Attacks on Electricity Markets by Limited Adversaries: Worst-Case Robustness