Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid Networks
Smart grids have become susceptible to cyber-attacks, being one of the most diversified cyber-physical systems. Measurements collected by the supervisory control and data acquisition system can be compromised by a smart hacker, who can cheat a bad-data detector during state estimation by injecting b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8967032/ |
| _version_ | 1818445546483875840 |
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| author | Mario R. Camana Acosta Saeed Ahmed Carla E. Garcia Insoo Koo |
| author_facet | Mario R. Camana Acosta Saeed Ahmed Carla E. Garcia Insoo Koo |
| author_sort | Mario R. Camana Acosta |
| collection | DOAJ |
| description | Smart grids have become susceptible to cyber-attacks, being one of the most diversified cyber-physical systems. Measurements collected by the supervisory control and data acquisition system can be compromised by a smart hacker, who can cheat a bad-data detector during state estimation by injecting biased values into the sensor-collected measurements. This may result in false control decisions, compromising the security of the smart grid, and leading to financial losses, power network disruptions, or a combination of both. To overcome these problems, we propose a novel approach to cyber-attacks detection, based on an extremely randomized trees algorithm and kernel principal component analysis for dimensionality reduction. A performance evaluation of the proposed scheme is done by using the standard IEEE 57-bus and 118-bus systems. Numerical results show that the proposed scheme outperforms state-of-art approaches while improving the accuracy in detection of stealth cyber-attacks in smart-grid measurements. |
| first_indexed | 2024-12-14T19:33:33Z |
| format | Article |
| id | doaj.art-0dab8acb6b3d4aa395333d2d3ec304f9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:33:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0dab8acb6b3d4aa395333d2d3ec304f92022-12-21T22:49:59ZengIEEEIEEE Access2169-35362020-01-018199211993310.1109/ACCESS.2020.29689348967032Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid NetworksMario R. Camana Acosta0https://orcid.org/0000-0002-1953-872XSaeed Ahmed1https://orcid.org/0000-0002-3624-4096Carla E. Garcia2https://orcid.org/0000-0003-4692-253XInsoo Koo3https://orcid.org/0000-0001-7476-8782School of Electrical and Computer Engineering, University of Ulsan, Ulsan, South KoreaSchool of Electrical and Computer Engineering, University of Ulsan, Ulsan, South KoreaSchool of Electrical and Computer Engineering, University of Ulsan, Ulsan, South KoreaSchool of Electrical and Computer Engineering, University of Ulsan, Ulsan, South KoreaSmart grids have become susceptible to cyber-attacks, being one of the most diversified cyber-physical systems. Measurements collected by the supervisory control and data acquisition system can be compromised by a smart hacker, who can cheat a bad-data detector during state estimation by injecting biased values into the sensor-collected measurements. This may result in false control decisions, compromising the security of the smart grid, and leading to financial losses, power network disruptions, or a combination of both. To overcome these problems, we propose a novel approach to cyber-attacks detection, based on an extremely randomized trees algorithm and kernel principal component analysis for dimensionality reduction. A performance evaluation of the proposed scheme is done by using the standard IEEE 57-bus and 118-bus systems. Numerical results show that the proposed scheme outperforms state-of-art approaches while improving the accuracy in detection of stealth cyber-attacks in smart-grid measurements.https://ieeexplore.ieee.org/document/8967032/Machine learningKPCAextra-treescyber-attackscyber-security |
| spellingShingle | Mario R. Camana Acosta Saeed Ahmed Carla E. Garcia Insoo Koo Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid Networks IEEE Access Machine learning KPCA extra-trees cyber-attacks cyber-security |
| title | Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid Networks |
| title_full | Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid Networks |
| title_fullStr | Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid Networks |
| title_full_unstemmed | Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid Networks |
| title_short | Extremely Randomized Trees-Based Scheme for Stealthy Cyber-Attack Detection in Smart Grid Networks |
| title_sort | extremely randomized trees based scheme for stealthy cyber attack detection in smart grid networks |
| topic | Machine learning KPCA extra-trees cyber-attacks cyber-security |
| url | https://ieeexplore.ieee.org/document/8967032/ |
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