Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid

Xu, Ran; Wang, Rui; Guan, Zhitao; Wu, Lina; Wu, Jun; Du, Xiaojiang

doi:10.1109/access.2017.2728681

Cited by 97 publications

(31 citation statements)

References 32 publications

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“…C9. Security Intrusion detection ( [8], [15], [52], [74], [76], [77], [143], [148], [209]),false data injection attacks ( [13], [16], [17], [31], [100], [119], [151], [163], [168], [184], [185], [230], [251], [257]), energy theft ( [54], [99], [169], [191], [254], [258]), distinguishing cyber-attacks from physical faults ( [18])…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…One of the well-known static detectors is detectors of attacks on state estimation. To date, several strategies have been developed to detect/mitigate FDI attacks targeting state estimation, such as statistical methods [2,106], Kalman filter [107], sparse optimization [108], state forecasting [109,110], network theory [111], time-series simulation [69], machine learning [112][113][114][115][116], generalized likelihood ratio [117], Chi-square detector, and similarity matching [118]. However, these strategies are used to recover DC state information and are suitable for FDI attacks on DC state estimation.…”

Section: Static Detectors Of Cyber-attacksmentioning

confidence: 99%

Cyber-Security of Smart Microgrids: A Survey

Nejabatkhah¹,

Liang

et al. 2020

Energies

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In this paper, the cyber-security of smart microgrids is thoroughly discussed. In smart grids, the cyber system and physical process are tightly coupled. Due to the cyber system’s vulnerabilities, any cyber incidents can have economic and physical impacts on their operations. In power electronics-intensive smart microgrids, cyber-attacks can have much more harmful and devastating effects on their operation and stability due to low inertia, especially in islanded operation. In this paper, the cyber–physical systems in smart microgrids are briefly studied. Then, the cyber-attacks on data availability, integrity, and confidentiality are discussed. Since a false data injection (FDI) attack that compromises the data integrity in the cyber/communication network is one of the most challenging threats for smart microgrids, it is investigated in detail in this paper. Such FDI attacks can target state estimation, voltage and frequency control, and smart microgrids’ protection systems. The economic and physical/technical impacts of the FDI attacks on smart microgrids are also reviewed in this paper. The defensive strategies against FDI attacks are classified into protection strategies, in which selected meter measurements are protected, and detection/mitigation strategies, based on either static or dynamic detection. In this paper, implementation examples of FDI attacks’ construction and detection/mitigation in smart microgrids are provided. Samples of recent cyber-security projects in the world, and critical cyber-security standards of smart grids, are presented. Finally, future trends of cyber-security in smart microgrids are discussed.

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“…There are currently many popular detection methods that have been developped that relies on special meaurements. Among those we will contrast our method with [48], which is a detection scheme using K-means clustering on CSSVC (Control Signal from the controller to the Static Var Compensator) and NVSI (Node Voltage Stability Index) measurements, and [49], that involves the use of SVD (Singular Value Decomposition) and convex-optimization in order exploit the low-rank property of the measurement matrix comprising of PMU measurements. Compared to those methods, the technique, proposed in this paper, has higher accuracy and is less prone to false positives since the entropic state will decrease as the attack is continuously propagated throughout the generative model.…”

Section: ) Complete Algorithmmentioning

confidence: 99%

Cognitive Dynamic System for Control and Cyber-Attack Detection in Smart Grid

Oozeer

Haykin

2019

IEEE Access

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This paper introduces a new way of thinking that characterizes itself by uniting two entities, namely state estimation in the smart grid (SG) and cognitive dynamic system (CDS). False data injection (FDI) attacks are a family of new attacks that have been considered to be the most dangerous cyber-attack as it leads to cascaded bad decision making throughout the SG network, which can lead to severe repercussions. The conventional state estimation and bad data detection techniques, which have been applied to reduce observation errors and detect bad data in energy system state estimators, cannot detect FDI attacks. Here, we bring into play an objective-seeking system to act as the supervisor of the SG network. To this end, we propose to introduce a new metric for the SG: the entropic state. The entropic state has two purposes: 1) it provides an indication of the grid's health on a cycle-to-cycle basis and 2) it can be used to detect FDI attacks. Consequently, improving the entropic state is the goal of the supervisor. To achieve that objective, the supervisor dynamically optimizes the state estimation process by reconfiguring the weights of the sensors in the network. With optimality in mind, the CDS is the superior choice for the supervisory system. In this structure, the CDS interacts with the SG network, which is considered as the environment. Computer simulations are carried out on a 4-bus and the IEEE 14-bus systems to highlight the performance of the proposed approach in detecting both bad data and FDI attacks in the SG, respectively.

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Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid

Cited by 97 publications

References 32 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

Cyber-Security of Smart Microgrids: A Survey

Cognitive Dynamic System for Control and Cyber-Attack Detection in Smart Grid

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