Towards a lightweight security framework using blockchain and machine learning
Cyber-attacks pose a significant challenge to the security of Internet of Things (IoT) sensor networks, necessitating the development of robust countermeasures tailored to their unique characteristics and limitations. Various prevention and detection techniques have been proposed to mitigate these a...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Blockchain: Research and Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096720923000490 |
| _version_ | 1827287962927235072 |
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| author | Shereen Ismail Muhammad Nouman Diana W. Dawoud Hassan Reza |
| author_facet | Shereen Ismail Muhammad Nouman Diana W. Dawoud Hassan Reza |
| author_sort | Shereen Ismail |
| collection | DOAJ |
| description | Cyber-attacks pose a significant challenge to the security of Internet of Things (IoT) sensor networks, necessitating the development of robust countermeasures tailored to their unique characteristics and limitations. Various prevention and detection techniques have been proposed to mitigate these attacks. In this paper, we propose an integrated security framework using blockchain and Machine Learning (ML) to protect IoT sensor networks. The framework consists of two modules: a blockchain prevention module and an ML detection module. The blockchain prevention module has two lightweight mechanisms: identity management and trust management. Identity management employs a lightweight Smart Contract (SC) to manage node registration and authentication, ensuring that unauthorized entities are prohibited from engaging in any tasks, while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network's lifetime and tracking historical node behaviors. Consensus and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance (VBFT) mechanism to ensure network reliability and integrity. The ML detection module utilizes the Light Gradient Boosting Machine (LightGBM) algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their impacts. We investigate the performance of several off-the-shelf ML algorithms, including Logistic Regression, Complement Naive Bayes, Nearest Centroid, and Stacking, using the WSN-DS dataset. LightGBM is selected following a detailed comparative analysis conducted using accuracy, precision, recall, F1-score, processing time, training time, prediction time, computational complexity, and Matthews Correlation Coefficient (MCC) evaluation metrics. |
| first_indexed | 2024-04-24T11:21:55Z |
| format | Article |
| id | doaj.art-e2d9ebd4d33d4ad59c65e2536fdb0464 |
| institution | Directory Open Access Journal |
| issn | 2666-9536 |
| language | English |
| last_indexed | 2024-04-24T11:21:55Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Blockchain: Research and Applications |
| spelling | doaj.art-e2d9ebd4d33d4ad59c65e2536fdb04642024-04-11T04:41:10ZengElsevierBlockchain: Research and Applications2666-95362024-03-0151100174Towards a lightweight security framework using blockchain and machine learningShereen Ismail0Muhammad Nouman1Diana W. Dawoud2Hassan Reza3School of Electrical Engineering and Computer Science, University of North Dakota, ND 58202, USA; Corresponding author.Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, PakistanCollege of Engineering and Information Technology, University of Dubai, Dubai, United Arab EmiratesSchool of Electrical Engineering and Computer Science, University of North Dakota, ND 58202, USACyber-attacks pose a significant challenge to the security of Internet of Things (IoT) sensor networks, necessitating the development of robust countermeasures tailored to their unique characteristics and limitations. Various prevention and detection techniques have been proposed to mitigate these attacks. In this paper, we propose an integrated security framework using blockchain and Machine Learning (ML) to protect IoT sensor networks. The framework consists of two modules: a blockchain prevention module and an ML detection module. The blockchain prevention module has two lightweight mechanisms: identity management and trust management. Identity management employs a lightweight Smart Contract (SC) to manage node registration and authentication, ensuring that unauthorized entities are prohibited from engaging in any tasks, while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network's lifetime and tracking historical node behaviors. Consensus and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance (VBFT) mechanism to ensure network reliability and integrity. The ML detection module utilizes the Light Gradient Boosting Machine (LightGBM) algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their impacts. We investigate the performance of several off-the-shelf ML algorithms, including Logistic Regression, Complement Naive Bayes, Nearest Centroid, and Stacking, using the WSN-DS dataset. LightGBM is selected following a detailed comparative analysis conducted using accuracy, precision, recall, F1-score, processing time, training time, prediction time, computational complexity, and Matthews Correlation Coefficient (MCC) evaluation metrics.http://www.sciencedirect.com/science/article/pii/S2096720923000490BlockchainMachine learningIoTSecurityIntegrationSmart contracts |
| spellingShingle | Shereen Ismail Muhammad Nouman Diana W. Dawoud Hassan Reza Towards a lightweight security framework using blockchain and machine learning Blockchain: Research and Applications Blockchain Machine learning IoT Security Integration Smart contracts |
| title | Towards a lightweight security framework using blockchain and machine learning |
| title_full | Towards a lightweight security framework using blockchain and machine learning |
| title_fullStr | Towards a lightweight security framework using blockchain and machine learning |
| title_full_unstemmed | Towards a lightweight security framework using blockchain and machine learning |
| title_short | Towards a lightweight security framework using blockchain and machine learning |
| title_sort | towards a lightweight security framework using blockchain and machine learning |
| topic | Blockchain Machine learning IoT Security Integration Smart contracts |
| url | http://www.sciencedirect.com/science/article/pii/S2096720923000490 |
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