Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks
This paper presents an innovative incentive model that utilizes graph and game theories to address the issue of node incentives in decentralized blockchain networks such as EVM blockchains. The lack of incentives for nodes within EVM networks gives rise to potential weaknesses that might be used for...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2224-2708/13/1/7 |
| _version_ | 1797297718381510656 |
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| author | Souhail Mssassi Anas Abou El Kalam |
| author_facet | Souhail Mssassi Anas Abou El Kalam |
| author_sort | Souhail Mssassi |
| collection | DOAJ |
| description | This paper presents an innovative incentive model that utilizes graph and game theories to address the issue of node incentives in decentralized blockchain networks such as EVM blockchains. The lack of incentives for nodes within EVM networks gives rise to potential weaknesses that might be used for various purposes, such as broadcasting fake transactions or withholding blocks. This affects the overall trust and integrity of the network. To address this issue, the current study offers a network model that incorporates the concepts of graph theory and utilizes a matrix representation for reward and trust optimization. Furthermore, this study presents a game-theoretic framework that encourages cooperative conduct and discourages malicious actions, ultimately producing a state of equilibrium according to the Nash equilibrium. The simulations validated the model’s efficacy in addressing fraudulent transactions and emphasized its scalability, security, and fairness benefits. This study makes a valuable contribution to the field of blockchain technology by presenting an incentive model that effectively encourages the development of secure and trusted decentralized systems. |
| first_indexed | 2024-03-07T22:24:25Z |
| format | Article |
| id | doaj.art-167e4eba94374e8a93d1f016201d81cc |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-03-07T22:24:25Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-167e4eba94374e8a93d1f016201d81cc2024-02-23T15:23:55ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082024-01-01131710.3390/jsan13010007Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain NetworksSouhail Mssassi0Anas Abou El Kalam1National School of Applied Sciences, Cadi Ayyad University, Marrakech 40000, MoroccoNational School of Applied Sciences, Cadi Ayyad University, Marrakech 40000, MoroccoThis paper presents an innovative incentive model that utilizes graph and game theories to address the issue of node incentives in decentralized blockchain networks such as EVM blockchains. The lack of incentives for nodes within EVM networks gives rise to potential weaknesses that might be used for various purposes, such as broadcasting fake transactions or withholding blocks. This affects the overall trust and integrity of the network. To address this issue, the current study offers a network model that incorporates the concepts of graph theory and utilizes a matrix representation for reward and trust optimization. Furthermore, this study presents a game-theoretic framework that encourages cooperative conduct and discourages malicious actions, ultimately producing a state of equilibrium according to the Nash equilibrium. The simulations validated the model’s efficacy in addressing fraudulent transactions and emphasized its scalability, security, and fairness benefits. This study makes a valuable contribution to the field of blockchain technology by presenting an incentive model that effectively encourages the development of secure and trusted decentralized systems.https://www.mdpi.com/2224-2708/13/1/7incentive modeldecentralized blockchain networksnode incentivesgraph theorygame theoryEVM networks |
| spellingShingle | Souhail Mssassi Anas Abou El Kalam Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks Journal of Sensor and Actuator Networks incentive model decentralized blockchain networks node incentives graph theory game theory EVM networks |
| title | Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks |
| title_full | Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks |
| title_fullStr | Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks |
| title_full_unstemmed | Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks |
| title_short | Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks |
| title_sort | game theory based incentive design for mitigating malicious behavior in blockchain networks |
| topic | incentive model decentralized blockchain networks node incentives graph theory game theory EVM networks |
| url | https://www.mdpi.com/2224-2708/13/1/7 |
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