Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods
False data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize expe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/16/2/156 |
| _version_ | 1797296917790588928 |
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| author | Bao Jin Xiaodong Zhao Dongmei Yuan |
| author_facet | Bao Jin Xiaodong Zhao Dongmei Yuan |
| author_sort | Bao Jin |
| collection | DOAJ |
| description | False data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize expected load losses and generator trips through load and power generation adjustments. The selection of strategies of the attacking and defending sides turns out to be a symmetric game process. This article proposes a hybrid game theory method for analyzing the attack–defense confrontation: firstly, a micro-grid-based power market model considering false data injection attacks is established using the Nash equilibrium method; secondly, the attack–defense game function is constructed and solved via the Stackelberg equilibrium algorithm. The Markov game algorithm and distributed learning algorithm are used to update equilibrium function; finally, a dynamic game behavior model of the two players is constructed through simulating the attack–defense probability. The evolutionary game method is used to select the optimal defense strategy for dynamic probability changes. Modified IEEE standard bus systems are illustrated to certify the effectiveness of the proposed model. |
| first_indexed | 2024-03-07T22:11:46Z |
| format | Article |
| id | doaj.art-1beca847ea464a8b910a1941adf06a27 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-07T22:11:46Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-1beca847ea464a8b910a1941adf06a272024-02-23T15:35:52ZengMDPI AGSymmetry2073-89942024-01-0116215610.3390/sym16020156Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic MethodsBao Jin0Xiaodong Zhao1Dongmei Yuan2Institute of Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Mathematics and Statistics, Taishan University, Tai’an 271000, ChinaCollege of Electric Engineering, Nanjing Xiaozhuang University, Nanjing 210023, ChinaFalse data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize expected load losses and generator trips through load and power generation adjustments. The selection of strategies of the attacking and defending sides turns out to be a symmetric game process. This article proposes a hybrid game theory method for analyzing the attack–defense confrontation: firstly, a micro-grid-based power market model considering false data injection attacks is established using the Nash equilibrium method; secondly, the attack–defense game function is constructed and solved via the Stackelberg equilibrium algorithm. The Markov game algorithm and distributed learning algorithm are used to update equilibrium function; finally, a dynamic game behavior model of the two players is constructed through simulating the attack–defense probability. The evolutionary game method is used to select the optimal defense strategy for dynamic probability changes. Modified IEEE standard bus systems are illustrated to certify the effectiveness of the proposed model.https://www.mdpi.com/2073-8994/16/2/156false data injection attackmicro-gridMarkov game algorithmdistributed learning algorithmevolutionary game methodoptimal defense strategy |
| spellingShingle | Bao Jin Xiaodong Zhao Dongmei Yuan Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods Symmetry false data injection attack micro-grid Markov game algorithm distributed learning algorithm evolutionary game method optimal defense strategy |
| title | Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods |
| title_full | Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods |
| title_fullStr | Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods |
| title_full_unstemmed | Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods |
| title_short | Attack–Defense Confrontation Analysis and Optimal Defense Strategy Selection Using Hybrid Game Theoretic Methods |
| title_sort | attack defense confrontation analysis and optimal defense strategy selection using hybrid game theoretic methods |
| topic | false data injection attack micro-grid Markov game algorithm distributed learning algorithm evolutionary game method optimal defense strategy |
| url | https://www.mdpi.com/2073-8994/16/2/156 |
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