A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection
In this research, we proposed a novel anomaly detection system (ADS) that integrates federated learning (FL) with blockchain for resource-constrained IoT. The proposed system allows IoT devices to exchange machine learning (ML) models through a permissioned blockchain, enabling trustworthy collabora...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11070312/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849428409348784128 |
|---|---|
| author | Van-Doan Nguyen Abebe Diro Naveen Chilamkurti Will Heyne Khoa Tran Phan |
| author_facet | Van-Doan Nguyen Abebe Diro Naveen Chilamkurti Will Heyne Khoa Tran Phan |
| author_sort | Van-Doan Nguyen |
| collection | DOAJ |
| description | In this research, we proposed a novel anomaly detection system (ADS) that integrates federated learning (FL) with blockchain for resource-constrained IoT. The proposed system allows IoT devices to exchange machine learning (ML) models through a permissioned blockchain, enabling trustworthy collaborative learning through model sharing. To avoid single-point failure, any device can be a centre of the FL process. To deal with the issue of resource constraints in IoT devices and the model poisoning problem in FL, we introduced a novel method to use commitment coefficients and ML model discrepancies when selecting particular devices to join the FL process. We also proposed an efficient heuristic method to aggregate a federated model from a list of ML models trained locally on the selected devices, which helps to improve the federated model’s anomaly detection ability. The experiment results with the popular N-BaIoT dataset for IoT botnet attack detection show that the proposed system is more effective in detecting anomalies and resisting poisoning attacks than the two baselines (FedProx and FedAvg). |
| format | Article |
| id | doaj-art-b4a40924c66f4127a345d8b23218e278 |
| institution | Kabale University |
| issn | 2831-316X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-b4a40924c66f4127a345d8b23218e2782025-08-20T03:28:43ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2025-01-01379881310.1109/TMLCN.2025.358584211070312A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly DetectionVan-Doan Nguyen0https://orcid.org/0000-0001-5834-3709Abebe Diro1https://orcid.org/0000-0001-7147-2783Naveen Chilamkurti2https://orcid.org/0000-0002-5396-8897Will Heyne3https://orcid.org/0009-0005-9230-5542Khoa Tran Phan4https://orcid.org/0000-0003-0471-9402Department of Computer Science and IT, La Trobe University, Melbourne, VIC, AustraliaCollege of Business and Law, RMIT University, Melbourne, VIC, AustraliaDepartment of Computer Science and IT, La Trobe University, Melbourne, VIC, AustraliaBAE Systems Australia, Adelaide, VIC, AustraliaDepartment of Computer Science and IT, La Trobe University, Melbourne, VIC, AustraliaIn this research, we proposed a novel anomaly detection system (ADS) that integrates federated learning (FL) with blockchain for resource-constrained IoT. The proposed system allows IoT devices to exchange machine learning (ML) models through a permissioned blockchain, enabling trustworthy collaborative learning through model sharing. To avoid single-point failure, any device can be a centre of the FL process. To deal with the issue of resource constraints in IoT devices and the model poisoning problem in FL, we introduced a novel method to use commitment coefficients and ML model discrepancies when selecting particular devices to join the FL process. We also proposed an efficient heuristic method to aggregate a federated model from a list of ML models trained locally on the selected devices, which helps to improve the federated model’s anomaly detection ability. The experiment results with the popular N-BaIoT dataset for IoT botnet attack detection show that the proposed system is more effective in detecting anomalies and resisting poisoning attacks than the two baselines (FedProx and FedAvg).https://ieeexplore.ieee.org/document/11070312/Anomaly detectionblockchaincybersecurityfederated learningInternet of Thingssatellite networks |
| spellingShingle | Van-Doan Nguyen Abebe Diro Naveen Chilamkurti Will Heyne Khoa Tran Phan A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection IEEE Transactions on Machine Learning in Communications and Networking Anomaly detection blockchain cybersecurity federated learning Internet of Things satellite networks |
| title | A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection |
| title_full | A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection |
| title_fullStr | A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection |
| title_full_unstemmed | A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection |
| title_short | A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection |
| title_sort | novel blockchain enabled federated learning scheme for iot anomaly detection |
| topic | Anomaly detection blockchain cybersecurity federated learning Internet of Things satellite networks |
| url | https://ieeexplore.ieee.org/document/11070312/ |
| work_keys_str_mv | AT vandoannguyen anovelblockchainenabledfederatedlearningschemeforiotanomalydetection AT abebediro anovelblockchainenabledfederatedlearningschemeforiotanomalydetection AT naveenchilamkurti anovelblockchainenabledfederatedlearningschemeforiotanomalydetection AT willheyne anovelblockchainenabledfederatedlearningschemeforiotanomalydetection AT khoatranphan anovelblockchainenabledfederatedlearningschemeforiotanomalydetection AT vandoannguyen novelblockchainenabledfederatedlearningschemeforiotanomalydetection AT abebediro novelblockchainenabledfederatedlearningschemeforiotanomalydetection AT naveenchilamkurti novelblockchainenabledfederatedlearningschemeforiotanomalydetection AT willheyne novelblockchainenabledfederatedlearningschemeforiotanomalydetection AT khoatranphan novelblockchainenabledfederatedlearningschemeforiotanomalydetection |