Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming
Smart farming influences advanced technologies to optimize agricultural procedures, yet it meets significant cybersecurity challenges, particularly in External Intrusion Detection (EID). This article proposes a novel architecture combining Blockchain Technology and Federated Learning (FL) to reinfor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025
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| Subjects: | |
| Online Access: | https://repository.londonmet.ac.uk/9708/1/Accepted%20version.pdf |
| _version_ | 1824446488880611328 |
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| author | Singh, Sushil Kumar Kumar, Manish Khanna, Ashish Virdee, Bal Singh |
| author_facet | Singh, Sushil Kumar Kumar, Manish Khanna, Ashish Virdee, Bal Singh |
| author_sort | Singh, Sushil Kumar |
| collection | LMU |
| description | Smart farming influences advanced technologies to optimize agricultural procedures, yet it meets significant cybersecurity challenges, particularly in External Intrusion Detection (EID). This article proposes a novel architecture combining Blockchain Technology and Federated Learning (FL) to reinforce the security of Smart Farming Systems (SMS) against external threats. The integration of Blockchain ensures data authentication and transparent data storage, while FL enables collaborative model training without compromising data privacy. Our architecture employs Ensemble Learning (EL) for the Local Model at the Ensemble Layer to train each Smart Land's data and offers privacy-prevented security. These devices utilize FL techniques to collaboratively train intrusion detection models while preserving the confidentiality of sensitive data. The Aggregated Model completes data aggregation at the Authentication Layer, and the PoAh Consensus Algorithm is leveraged for smart land's data authentication. The IoT Sensor device's identical information of smart lands is stored at the Macro Base Stations (MBSs). After downloading the aggregated values of the aggregated model, the local model transfers the smart lands information to the Cloud layer for decision making and decentralized storage. The validation outcomes of the proposed architecture demonstrate excellent performance, with an average processing time of 3.663 secs and 0.9956 accuracy for Smart Land compared to existing frameworks. |
| first_indexed | 2025-02-19T01:15:58Z |
| format | Article |
| id | oai:repository.londonmet.ac.uk:9708 |
| institution | London Metropolitan University |
| language | English |
| last_indexed | 2025-02-19T01:15:58Z |
| publishDate | 2025 |
| publisher | IEEE |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:97082025-02-04T02:50:47Z https://repository.londonmet.ac.uk/9708/ Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming Singh, Sushil Kumar Kumar, Manish Khanna, Ashish Virdee, Bal Singh 000 Computer science, information & general works Smart farming influences advanced technologies to optimize agricultural procedures, yet it meets significant cybersecurity challenges, particularly in External Intrusion Detection (EID). This article proposes a novel architecture combining Blockchain Technology and Federated Learning (FL) to reinforce the security of Smart Farming Systems (SMS) against external threats. The integration of Blockchain ensures data authentication and transparent data storage, while FL enables collaborative model training without compromising data privacy. Our architecture employs Ensemble Learning (EL) for the Local Model at the Ensemble Layer to train each Smart Land's data and offers privacy-prevented security. These devices utilize FL techniques to collaboratively train intrusion detection models while preserving the confidentiality of sensitive data. The Aggregated Model completes data aggregation at the Authentication Layer, and the PoAh Consensus Algorithm is leveraged for smart land's data authentication. The IoT Sensor device's identical information of smart lands is stored at the Macro Base Stations (MBSs). After downloading the aggregated values of the aggregated model, the local model transfers the smart lands information to the Cloud layer for decision making and decentralized storage. The validation outcomes of the proposed architecture demonstrate excellent performance, with an average processing time of 3.663 secs and 0.9956 accuracy for Smart Land compared to existing frameworks. IEEE 2025-02-01 Article PeerReviewed text en cc_by_4 https://repository.londonmet.ac.uk/9708/1/Accepted%20version.pdf Singh, Sushil Kumar, Kumar, Manish, Khanna, Ashish and Virdee, Bal Singh (2025) Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming. IEEE Internet of Things Journal, 12 (3). pp. 3297-3304. ISSN 2327-4662 https://ieeexplore.ieee.org/document/10714358 10.1109/JIOT.2024.3478820 10.1109/JIOT.2024.3478820 |
| spellingShingle | 000 Computer science, information & general works Singh, Sushil Kumar Kumar, Manish Khanna, Ashish Virdee, Bal Singh Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming |
| title | Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming |
| title_full | Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming |
| title_fullStr | Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming |
| title_full_unstemmed | Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming |
| title_short | Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming |
| title_sort | blockchain and fl based secure architecture for enhanced external intrusion detection in smart farming |
| topic | 000 Computer science, information & general works |
| url | https://repository.londonmet.ac.uk/9708/1/Accepted%20version.pdf |
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