Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection
The Industrial Internet of Things (IIoT) faces significant cybersecurity threats due to its ever-changing network structures, diverse data sources, and inherent uncertainties, making robust intrusion detection crucial. Conventional machine learning methods and typical Graph Neural Networks (GNNs) of...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11075530/ |
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| author | Safa Ben Atitallah Maha Driss Wadii Boulila Anis Koubaa |
| author_facet | Safa Ben Atitallah Maha Driss Wadii Boulila Anis Koubaa |
| author_sort | Safa Ben Atitallah |
| collection | DOAJ |
| description | The Industrial Internet of Things (IIoT) faces significant cybersecurity threats due to its ever-changing network structures, diverse data sources, and inherent uncertainties, making robust intrusion detection crucial. Conventional machine learning methods and typical Graph Neural Networks (GNNs) often struggle to capture the complexity and uncertainty in IIoT network traffic, which hampers their effectiveness in detecting intrusions. To address these limitations, we propose the Fuzzy Graph Attention Network (FGATN), a novel intrusion detection framework that fuses fuzzy logic, graph attention mechanisms, and GNNs to deliver high accuracy and robustness in IIoT environments. FGATN introduces three core innovations: (1) fuzzy membership functions to explicitly model uncertainty and imprecision in traffic features; (2) fuzzy similarity-based graph construction with adaptive edge pruning to build meaningful graph topologies that reflect real-world communication patterns; and (3) an attention-guided fuzzy graph convolution mechanism that dynamically prioritizes reliable and task-relevant neighbors during message passing. We evaluate FGATN on three public intrusion datasets, Edge-IIoTSet, WSN-DS, and CIC-Malmem-2022, achieving accuracies of 99.07%, 99.20%, and 99.05%, respectively. It consistently outperforms state-of-the-art GNN (GCN, GraphSAGE, FGCN) and deep learning models (DNN, GRU, RobustCBL). Ablation studies confirm the essential roles of both fuzzy logic and attention mechanisms in boosting detection accuracy. Furthermore, FGATN demonstrates strong scalability, maintaining high performance across a range of varying graph sizes. These results highlight FGATN as a robust and scalable solution for next-generation IIoT intrusion detection systems. |
| format | Article |
| id | doaj-art-0f732d259ed645fcaae0c39a9200d8dc |
| institution | Kabale University |
| issn | 2644-1268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-0f732d259ed645fcaae0c39a9200d8dc2025-08-20T03:51:29ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-0161065107610.1109/OJCS.2025.358748611075530Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion DetectionSafa Ben Atitallah0https://orcid.org/0000-0003-0796-3507Maha Driss1https://orcid.org/0000-0001-8236-8746Wadii Boulila2https://orcid.org/0000-0003-2133-0757Anis Koubaa3Robotics and Internet of Things Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaRobotics and Internet of Things Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaRobotics and Internet of Things Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaAlfaisal University, Riyadh, Saudi ArabiaThe Industrial Internet of Things (IIoT) faces significant cybersecurity threats due to its ever-changing network structures, diverse data sources, and inherent uncertainties, making robust intrusion detection crucial. Conventional machine learning methods and typical Graph Neural Networks (GNNs) often struggle to capture the complexity and uncertainty in IIoT network traffic, which hampers their effectiveness in detecting intrusions. To address these limitations, we propose the Fuzzy Graph Attention Network (FGATN), a novel intrusion detection framework that fuses fuzzy logic, graph attention mechanisms, and GNNs to deliver high accuracy and robustness in IIoT environments. FGATN introduces three core innovations: (1) fuzzy membership functions to explicitly model uncertainty and imprecision in traffic features; (2) fuzzy similarity-based graph construction with adaptive edge pruning to build meaningful graph topologies that reflect real-world communication patterns; and (3) an attention-guided fuzzy graph convolution mechanism that dynamically prioritizes reliable and task-relevant neighbors during message passing. We evaluate FGATN on three public intrusion datasets, Edge-IIoTSet, WSN-DS, and CIC-Malmem-2022, achieving accuracies of 99.07%, 99.20%, and 99.05%, respectively. It consistently outperforms state-of-the-art GNN (GCN, GraphSAGE, FGCN) and deep learning models (DNN, GRU, RobustCBL). Ablation studies confirm the essential roles of both fuzzy logic and attention mechanisms in boosting detection accuracy. Furthermore, FGATN demonstrates strong scalability, maintaining high performance across a range of varying graph sizes. These results highlight FGATN as a robust and scalable solution for next-generation IIoT intrusion detection systems.https://ieeexplore.ieee.org/document/11075530/Attention mechanismsfuzzy logicGNNsindustrial IoTintrusion detection |
| spellingShingle | Safa Ben Atitallah Maha Driss Wadii Boulila Anis Koubaa Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection IEEE Open Journal of the Computer Society Attention mechanisms fuzzy logic GNNs industrial IoT intrusion detection |
| title | Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection |
| title_full | Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection |
| title_fullStr | Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection |
| title_full_unstemmed | Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection |
| title_short | Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection |
| title_sort | securing industrial iot environments a fuzzy graph attention network for robust intrusion detection |
| topic | Attention mechanisms fuzzy logic GNNs industrial IoT intrusion detection |
| url | https://ieeexplore.ieee.org/document/11075530/ |
| work_keys_str_mv | AT safabenatitallah securingindustrialiotenvironmentsafuzzygraphattentionnetworkforrobustintrusiondetection AT mahadriss securingindustrialiotenvironmentsafuzzygraphattentionnetworkforrobustintrusiondetection AT wadiiboulila securingindustrialiotenvironmentsafuzzygraphattentionnetworkforrobustintrusiondetection AT aniskoubaa securingindustrialiotenvironmentsafuzzygraphattentionnetworkforrobustintrusiondetection |