SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments - Kamran Ahmad Awan; Ikram Ud Din; Ahmad Almogren; Ali Nawaz; Muhammad Yasar Khan; Ayman Altameem (Heliyon)
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.