Secure Trust-Based Delegated Consensus for Blockchain Frameworks Using Deep Reinforcement Learning
Internet of Things (IoT) networks generate massive amounts of data while supporting various applications, where the security and protection of IoT data are very important. In particular, blockchain technology supporting IoT networks is considered as the most secure, expandable, and scalable database...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9943546/ |
| _version_ | 1797689575212056576 |
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| author | Yunyeong Goh Jusik Yun Dongjun Jung Jong-Moon Chung |
| author_facet | Yunyeong Goh Jusik Yun Dongjun Jung Jong-Moon Chung |
| author_sort | Yunyeong Goh |
| collection | DOAJ |
| description | Internet of Things (IoT) networks generate massive amounts of data while supporting various applications, where the security and protection of IoT data are very important. In particular, blockchain technology supporting IoT networks is considered as the most secure, expandable, and scalable database storage solution. However, existing blockchain systems have scalability problems due to low throughput and high resource consumption, and security problems due to malicious attacks. Several studies have proposed blockchain technologies that can improve the scalability or the security level, but there have been few studies that improve both at the same time. In addition, most existing studies do not consider malicious attack scenarios in the consensus process, which deteriorates the blockchain security level. In order to solve the scalability and security problems simultaneously, this paper proposes a Dueling Double Deep-Q-network with Prioritized experience replay (D3P) based secure trust-based delegated consensus blockchain (TDCB-D3P) scheme that optimizes the blockchain performance by applying deep reinforcement learning (DRL) technology. The TDCB-D3P scheme uses a trust system with a delegated consensus algorithm to ensure the security level and reduce computing costs. In addition, DRL is used to compute the optimum blockchain parameters under the dynamic network state and maximize the transactions per second (TPS) performance and security level. The simulation results show that the TDCB-D3P scheme can provide a superior TPS and resource consumption performance. Furthermore, in blockchain networks with malicious nodes, the simulation results show that the proposed scheme significantly improves the security level when compared to existing blockchain schemes by effectively reducing the influence of malicious nodes. |
| first_indexed | 2024-03-12T01:47:34Z |
| format | Article |
| id | doaj.art-91dda37a81094049ae17ac1237a63b5a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T01:47:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-91dda37a81094049ae17ac1237a63b5a2023-09-08T23:01:49ZengIEEEIEEE Access2169-35362022-01-011011849811851110.1109/ACCESS.2022.32208529943546Secure Trust-Based Delegated Consensus for Blockchain Frameworks Using Deep Reinforcement LearningYunyeong Goh0https://orcid.org/0000-0002-5000-2720Jusik Yun1https://orcid.org/0000-0003-3548-487XDongjun Jung2https://orcid.org/0000-0001-7108-6110Jong-Moon Chung3https://orcid.org/0000-0002-1652-6635School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South KoreaSchool of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South KoreaSchool of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South KoreaSchool of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South KoreaInternet of Things (IoT) networks generate massive amounts of data while supporting various applications, where the security and protection of IoT data are very important. In particular, blockchain technology supporting IoT networks is considered as the most secure, expandable, and scalable database storage solution. However, existing blockchain systems have scalability problems due to low throughput and high resource consumption, and security problems due to malicious attacks. Several studies have proposed blockchain technologies that can improve the scalability or the security level, but there have been few studies that improve both at the same time. In addition, most existing studies do not consider malicious attack scenarios in the consensus process, which deteriorates the blockchain security level. In order to solve the scalability and security problems simultaneously, this paper proposes a Dueling Double Deep-Q-network with Prioritized experience replay (D3P) based secure trust-based delegated consensus blockchain (TDCB-D3P) scheme that optimizes the blockchain performance by applying deep reinforcement learning (DRL) technology. The TDCB-D3P scheme uses a trust system with a delegated consensus algorithm to ensure the security level and reduce computing costs. In addition, DRL is used to compute the optimum blockchain parameters under the dynamic network state and maximize the transactions per second (TPS) performance and security level. The simulation results show that the TDCB-D3P scheme can provide a superior TPS and resource consumption performance. Furthermore, in blockchain networks with malicious nodes, the simulation results show that the proposed scheme significantly improves the security level when compared to existing blockchain schemes by effectively reducing the influence of malicious nodes.https://ieeexplore.ieee.org/document/9943546/Blockchainconsensus algorithmdeep reinforcement learning (DRL)Internet of Things (IoT)trust |
| spellingShingle | Yunyeong Goh Jusik Yun Dongjun Jung Jong-Moon Chung Secure Trust-Based Delegated Consensus for Blockchain Frameworks Using Deep Reinforcement Learning IEEE Access Blockchain consensus algorithm deep reinforcement learning (DRL) Internet of Things (IoT) trust |
| title | Secure Trust-Based Delegated Consensus for Blockchain Frameworks Using Deep Reinforcement Learning |
| title_full | Secure Trust-Based Delegated Consensus for Blockchain Frameworks Using Deep Reinforcement Learning |
| title_fullStr | Secure Trust-Based Delegated Consensus for Blockchain Frameworks Using Deep Reinforcement Learning |
| title_full_unstemmed | Secure Trust-Based Delegated Consensus for Blockchain Frameworks Using Deep Reinforcement Learning |
| title_short | Secure Trust-Based Delegated Consensus for Blockchain Frameworks Using Deep Reinforcement Learning |
| title_sort | secure trust based delegated consensus for blockchain frameworks using deep reinforcement learning |
| topic | Blockchain consensus algorithm deep reinforcement learning (DRL) Internet of Things (IoT) trust |
| url | https://ieeexplore.ieee.org/document/9943546/ |
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