SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments
The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a no...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024169058 |
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| author | Kamran Ahmad Awan Ikram Ud Din Ahmad Almogren Ali Nawaz Muhammad Yasar Khan Ayman Altameem |
| author_facet | Kamran Ahmad Awan Ikram Ud Din Ahmad Almogren Ali Nawaz Muhammad Yasar Khan Ayman Altameem |
| author_sort | Kamran Ahmad Awan |
| collection | DOAJ |
| description | The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments. The SecEdge framework integrates transformer-based models for efficient handling of long-range dependencies and Graph Neural Networks (GNNs) for modeling relational data, coupled with federated learning to ensure data privacy and reduce latency. The adaptive learning mechanism continuously updates model parameters to counter evolving cyber threats. The framework's performance was evaluated in a simulation environment using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. Results demonstrated that SecEdge outperformed state-of-the-art methods with a detection rate of 98.8% for DoS attacks on NSL-KDD, 98.5% for MitM attacks on UNSW-NB15, and 98.7% for data injection attacks on CICIDS2017. |
| format | Article |
| id | doaj-art-2ca6b6357f3a4287a8664a76dfdc29da |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-2ca6b6357f3a4287a8664a76dfdc29da2025-01-17T04:49:50ZengElsevierHeliyon2405-84402025-01-01111e40874SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environmentsKamran Ahmad Awan0Ikram Ud Din1Ahmad Almogren2Ali Nawaz3Muhammad Yasar Khan4Ayman Altameem5Department of Information Technology, The University of Haripur, Haripur, 22620, Khyber Pakhtunkhwa, PakistanDepartment of Information Technology, The University of Haripur, Haripur, 22620, Khyber Pakhtunkhwa, PakistanDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi ArabiaCollege of Information Technology, United Arab Emirates University, Al Ain, 15551, United Arab Emirates; Co-corresponding author.SFI-Funded E-Governance Unit, Insight Centre for Data Analytics, University of Galway, Galway, H91 CF50, Ireland; Corresponding author.Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, Riyadh, 11633, Saudi ArabiaThe rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments. The SecEdge framework integrates transformer-based models for efficient handling of long-range dependencies and Graph Neural Networks (GNNs) for modeling relational data, coupled with federated learning to ensure data privacy and reduce latency. The adaptive learning mechanism continuously updates model parameters to counter evolving cyber threats. The framework's performance was evaluated in a simulation environment using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. Results demonstrated that SecEdge outperformed state-of-the-art methods with a detection rate of 98.8% for DoS attacks on NSL-KDD, 98.5% for MitM attacks on UNSW-NB15, and 98.7% for data injection attacks on CICIDS2017.http://www.sciencedirect.com/science/article/pii/S2405844024169058Deep learningCyber securityMobile computingInternet of thingsAnamoly detectionGraph neural network |
| spellingShingle | Kamran Ahmad Awan Ikram Ud Din Ahmad Almogren Ali Nawaz Muhammad Yasar Khan Ayman Altameem SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments Heliyon Deep learning Cyber security Mobile computing Internet of things Anamoly detection Graph neural network |
| title | SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments |
| title_full | SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments |
| title_fullStr | SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments |
| title_full_unstemmed | SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments |
| title_short | SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments |
| title_sort | secedge a novel deep learning framework for real time cybersecurity in mobile iot environments |
| topic | Deep learning Cyber security Mobile computing Internet of things Anamoly detection Graph neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024169058 |
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