Survey of machine learning methods for detecting false data injection attacks in power systems
Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs t...
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| Published in | IET Smart Grid Vol. 3; no. 5; pp. 581 - 595 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Durham
The Institution of Engineering and Technology
01.10.2020
John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2515-2947 2515-2947 |
| DOI | 10.1049/iet-stg.2020.0015 |
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| Abstract | Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber-attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs to manipulate the power system state estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include bad data detection algorithms to eliminate errors from the acquired measurements, e.g. in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. To overcome the limitations of traditional residual-based BDD approaches, data-driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This study provides a comprehensive review of the most up-to-date machine learning methods for detecting FDIAs against power system SE algorithms. |
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| AbstractList | Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false data injection attacks (FDIAs) are a class of cyber‐attacks against power grid monitoring systems. Adversaries can successfully perform FDIAs to manipulate the power system state estimation (SE) by compromising sensors or modifying system data. SE is an essential process performed by the energy management system towards estimating unknown state variables based on system redundant measurements and network topology. SE routines include bad data detection algorithms to eliminate errors from the acquired measurements, e.g. in case of sensor failures. FDIAs can bypass BDD modules to inject malicious data vectors into a subset of measurements without being detected, and thus manipulate the results of the SE process. To overcome the limitations of traditional residual‐based BDD approaches, data‐driven solutions based on machine learning algorithms have been widely adopted for detecting malicious manipulation of sensor data due to their fast execution times and accurate results. This study provides a comprehensive review of the most up‐to‐date machine learning methods for detecting FDIAs against power system SE algorithms. |
| Author | Zografopoulos, Ioannis Jin, Yier Liu, XiaoRui Hu, Yaodan Sayghe, Ali Dutta, Raj Gautam Konstantinou, Charalambos |
| Author_xml | – sequence: 1 givenname: Ali orcidid: 0000-0003-2145-1671 surname: Sayghe fullname: Sayghe, Ali organization: 1FAMU-FSU College of Engineering, Center for Advanced Power Systems, Florida State University, Tallahassee, FL, USA – sequence: 2 givenname: Yaodan surname: Hu fullname: Hu, Yaodan organization: 2Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA – sequence: 3 givenname: Ioannis surname: Zografopoulos fullname: Zografopoulos, Ioannis organization: 1FAMU-FSU College of Engineering, Center for Advanced Power Systems, Florida State University, Tallahassee, FL, USA – sequence: 4 givenname: XiaoRui surname: Liu fullname: Liu, XiaoRui organization: 1FAMU-FSU College of Engineering, Center for Advanced Power Systems, Florida State University, Tallahassee, FL, USA – sequence: 5 givenname: Raj Gautam surname: Dutta fullname: Dutta, Raj Gautam organization: 2Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA – sequence: 6 givenname: Yier surname: Jin fullname: Jin, Yier organization: 2Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA – sequence: 7 givenname: Charalambos orcidid: 0000-0002-3825-3930 surname: Konstantinou fullname: Konstantinou, Charalambos email: ckonstantinou@ieee.org organization: 1FAMU-FSU College of Engineering, Center for Advanced Power Systems, Florida State University, Tallahassee, FL, USA |
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| Keywords | energy management system system redundant measurements power system measurement sensor data cyber-attacks binary decision diagrams data-driven solutions power grid monitoring systems power grids learning (artificial intelligence) machine learning algorithms false data injection attacks cyber attacks data detection algorithms power system state estimation unknown state variables power engineering computing power system security security of data energy management systems malicious data vectors FDIA power systems system data power system SE algorithms |
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| Snippet | Over the last decade, the number of cyber attacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, false... |
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| SubjectTerms | Algorithms Approximation binary decision diagrams Buses cyber attacks Damage detection Data acquisition data detection algorithms data-driven solutions Electricity distribution Energy management energy management system energy management systems false data injection attacks FDIA learning (artificial intelligence) Machine learning machine learning algorithms malicious data vectors Network topologies power engineering computing power grid monitoring systems power grids power system measurement power system SE algorithms power system security power system state estimation power systems security of data sensor data Special Section: Privacy and Security in Smart Grids State estimation system data system redundant measurements unknown state variables Variables |
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| Title | Survey of machine learning methods for detecting false data injection attacks in power systems |
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