Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy
Abstract The rapid expansion of IoT devices has introduced significant cybersecurity risks, as attackers increasingly exploit these networks’ vulnerabilities. To counter this threat, this paper presents the Privacy-Enhanced IoT Defence System (PEIoT-DS), a novel solution that emphasises data privacy...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Internet of Things |
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| Online Access: | https://doi.org/10.1007/s43926-025-00169-7 |
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| _version_ | 1850207509671837696 |
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| author | Ansam Khraisat Ammar Alazab Moutaz Alazab Areej Obeidat Sarabjot Singh Tony Jan |
| author_facet | Ansam Khraisat Ammar Alazab Moutaz Alazab Areej Obeidat Sarabjot Singh Tony Jan |
| author_sort | Ansam Khraisat |
| collection | DOAJ |
| description | Abstract The rapid expansion of IoT devices has introduced significant cybersecurity risks, as attackers increasingly exploit these networks’ vulnerabilities. To counter this threat, this paper presents the Privacy-Enhanced IoT Defence System (PEIoT-DS), a novel solution that emphasises data privacy while delivering high-performance intrusion detection. PEIoT-DS use federated learning to create a comprehensive intrusion detection model without necessitating the transmission of raw data to a central server. IoT devices only contribute model updates, which are then combined to improve the global model. While allowing devices to benefit from the network’s collective insights, this decentralised learning methodology safeguards data privacy. Using a real-world IoT dataset and two popular federated learning algorithms—Federated Average and Federated Average with Momentum—the study assesses the effectiveness of PEIoT-DS. The findings show that, in comparison to Federated Average, Federated Average with Momentum produces faster convergence and better intrusion detection accuracy. Our PEIoT-DS approach offers a reliable intrusion detection system for IoT networks while maintaining privacy. |
| format | Article |
| id | doaj-art-3a09d9db9ab3452c99e46bca8d2cdf40 |
| institution | OA Journals |
| issn | 2730-7239 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Internet of Things |
| spelling | doaj-art-3a09d9db9ab3452c99e46bca8d2cdf402025-08-20T02:10:31ZengSpringerDiscover Internet of Things2730-72392025-06-015111710.1007/s43926-025-00169-7Federated learning for intrusion detection in IoT environments: a privacy-preserving strategyAnsam Khraisat0Ammar Alazab1Moutaz Alazab2Areej Obeidat3Sarabjot Singh4Tony Jan5Deakin Cyber Research and Innovation Centre, Deakin UniversityCentre for Artificial Intelligence and Optimization, Torrens UniversityFaculty of Artificial Intelligence, Al-Balqa Applied University (BAU)Independent ResearcherDeakin Cyber Research and Innovation Centre, Deakin UniversityCentre for Artificial Intelligence and Optimization, Torrens UniversityAbstract The rapid expansion of IoT devices has introduced significant cybersecurity risks, as attackers increasingly exploit these networks’ vulnerabilities. To counter this threat, this paper presents the Privacy-Enhanced IoT Defence System (PEIoT-DS), a novel solution that emphasises data privacy while delivering high-performance intrusion detection. PEIoT-DS use federated learning to create a comprehensive intrusion detection model without necessitating the transmission of raw data to a central server. IoT devices only contribute model updates, which are then combined to improve the global model. While allowing devices to benefit from the network’s collective insights, this decentralised learning methodology safeguards data privacy. Using a real-world IoT dataset and two popular federated learning algorithms—Federated Average and Federated Average with Momentum—the study assesses the effectiveness of PEIoT-DS. The findings show that, in comparison to Federated Average, Federated Average with Momentum produces faster convergence and better intrusion detection accuracy. Our PEIoT-DS approach offers a reliable intrusion detection system for IoT networks while maintaining privacy.https://doi.org/10.1007/s43926-025-00169-7Federated learningData privacyCommunication network securityAnomaly detectionIntrusion detection system |
| spellingShingle | Ansam Khraisat Ammar Alazab Moutaz Alazab Areej Obeidat Sarabjot Singh Tony Jan Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy Discover Internet of Things Federated learning Data privacy Communication network security Anomaly detection Intrusion detection system |
| title | Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy |
| title_full | Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy |
| title_fullStr | Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy |
| title_full_unstemmed | Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy |
| title_short | Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy |
| title_sort | federated learning for intrusion detection in iot environments a privacy preserving strategy |
| topic | Federated learning Data privacy Communication network security Anomaly detection Intrusion detection system |
| url | https://doi.org/10.1007/s43926-025-00169-7 |
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