SA-FLIDS: secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcare
Smart healthcare systems are gaining increased practicality and utility, driven by continuous advancements in artificial intelligence technologies, cloud and fog computing, and the Internet of Things (IoT). However, despite these transformative developments, challenges persist within IoT devices, en...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2024-12-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2414.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846121648183312384 |
|---|---|
| author | Radjaa Bensaid Nabila Labraoui Ado Adamou Abba Ari Hafida Saidi Joel Herve Mboussam Emati Leandros Maglaras |
| author_facet | Radjaa Bensaid Nabila Labraoui Ado Adamou Abba Ari Hafida Saidi Joel Herve Mboussam Emati Leandros Maglaras |
| author_sort | Radjaa Bensaid |
| collection | DOAJ |
| description | Smart healthcare systems are gaining increased practicality and utility, driven by continuous advancements in artificial intelligence technologies, cloud and fog computing, and the Internet of Things (IoT). However, despite these transformative developments, challenges persist within IoT devices, encompassing computational constraints, storage limitations, and attack vulnerability. These attacks target sensitive health information, compromise data integrity, and pose obstacles to the overall resilience of the healthcare sector. To address these vulnerabilities, Network-based Intrusion Detection Systems (NIDSs) are crucial in fortifying smart healthcare networks and ensuring secure use of IoMT-based applications by mitigating security risks. Thus, this article proposes a novel Secure and Authenticated Federated Learning-based NIDS framework using Blockchain (SA-FLIDS) for fog-IoMT-enabled smart healthcare systems. Our research aims to improve data privacy and reduce communication costs. Furthermore, we also address weaknesses in decentralized learning systems, like Sybil and Model Poisoning attacks. We leverage the blockchain-based Self-Sovereign Identity (SSI) model to handle client authentication and secure communication. Additionally, we use the Trimmed Mean method to aggregate data. This helps reduce the effect of unusual or malicious inputs when creating the overall model. Our approach is evaluated on real IoT traffic datasets such as CICIoT2023 and EdgeIIoTset. It demonstrates exceptional robustness against adversarial attacks. These findings underscore the potential of our technique to improve the security of IoMT-based healthcare applications. |
| format | Article |
| id | doaj-art-62393eec18d24ae5b9a57e917f8dcdd8 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-62393eec18d24ae5b9a57e917f8dcdd82024-12-15T15:05:05ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e241410.7717/peerj-cs.2414SA-FLIDS: secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcareRadjaa Bensaid0Nabila Labraoui1Ado Adamou Abba Ari2Hafida Saidi3Joel Herve Mboussam Emati4Leandros Maglaras5STIC Lab, Abou Bekr Belkaid Tlemcen University, Tlemcen, AlgeriaLRI Lab, Abou Bekr Belkaid Tlemcen University, Tlemcen, AlgeriaLaRI Lab, University of Maroua, Cameroon, CameroonSTIC Lab, Abou Bekr Belkaid Tlemcen University, Tlemcen, AlgeriaDepartment of Mathematics and Computer Sciences, University of Dschang, CameroonSchool of Computer Science, Edinburgh Napier University, Edinburgh, United KingdomSmart healthcare systems are gaining increased practicality and utility, driven by continuous advancements in artificial intelligence technologies, cloud and fog computing, and the Internet of Things (IoT). However, despite these transformative developments, challenges persist within IoT devices, encompassing computational constraints, storage limitations, and attack vulnerability. These attacks target sensitive health information, compromise data integrity, and pose obstacles to the overall resilience of the healthcare sector. To address these vulnerabilities, Network-based Intrusion Detection Systems (NIDSs) are crucial in fortifying smart healthcare networks and ensuring secure use of IoMT-based applications by mitigating security risks. Thus, this article proposes a novel Secure and Authenticated Federated Learning-based NIDS framework using Blockchain (SA-FLIDS) for fog-IoMT-enabled smart healthcare systems. Our research aims to improve data privacy and reduce communication costs. Furthermore, we also address weaknesses in decentralized learning systems, like Sybil and Model Poisoning attacks. We leverage the blockchain-based Self-Sovereign Identity (SSI) model to handle client authentication and secure communication. Additionally, we use the Trimmed Mean method to aggregate data. This helps reduce the effect of unusual or malicious inputs when creating the overall model. Our approach is evaluated on real IoT traffic datasets such as CICIoT2023 and EdgeIIoTset. It demonstrates exceptional robustness against adversarial attacks. These findings underscore the potential of our technique to improve the security of IoMT-based healthcare applications.https://peerj.com/articles/cs-2414.pdfIntrusion detectionCybersecuritySmart healthcare |
| spellingShingle | Radjaa Bensaid Nabila Labraoui Ado Adamou Abba Ari Hafida Saidi Joel Herve Mboussam Emati Leandros Maglaras SA-FLIDS: secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcare PeerJ Computer Science Intrusion detection Cybersecurity Smart healthcare |
| title | SA-FLIDS: secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcare |
| title_full | SA-FLIDS: secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcare |
| title_fullStr | SA-FLIDS: secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcare |
| title_full_unstemmed | SA-FLIDS: secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcare |
| title_short | SA-FLIDS: secure and authenticated federated learning-based intelligent network intrusion detection system for smart healthcare |
| title_sort | sa flids secure and authenticated federated learning based intelligent network intrusion detection system for smart healthcare |
| topic | Intrusion detection Cybersecurity Smart healthcare |
| url | https://peerj.com/articles/cs-2414.pdf |
| work_keys_str_mv | AT radjaabensaid saflidssecureandauthenticatedfederatedlearningbasedintelligentnetworkintrusiondetectionsystemforsmarthealthcare AT nabilalabraoui saflidssecureandauthenticatedfederatedlearningbasedintelligentnetworkintrusiondetectionsystemforsmarthealthcare AT adoadamouabbaari saflidssecureandauthenticatedfederatedlearningbasedintelligentnetworkintrusiondetectionsystemforsmarthealthcare AT hafidasaidi saflidssecureandauthenticatedfederatedlearningbasedintelligentnetworkintrusiondetectionsystemforsmarthealthcare AT joelhervemboussamemati saflidssecureandauthenticatedfederatedlearningbasedintelligentnetworkintrusiondetectionsystemforsmarthealthcare AT leandrosmaglaras saflidssecureandauthenticatedfederatedlearningbasedintelligentnetworkintrusiondetectionsystemforsmarthealthcare |