Awan Kamran Ahmad, Ud Din Ikram, Almogren Ahmad, Nawaz Ali, Khan Muhammad Yasar, Altameem Ayman
Department of Information Technology, The University of Haripur, Haripur, 22620, Khyber Pakhtunkhwa, Pakistan.
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11633, Saudi Arabia.
Heliyon. 2024 Dec 5;11(1):e40874. doi: 10.1016/j.heliyon.2024.e40874. eCollection 2025 Jan 15.
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.
物联网（IoT）设备的快速增长因其多样且资源受限的特性带来了重大的网络安全挑战。现有的安全解决方案在应对物联网系统的动态和分布式环境时往往存在不足。本研究旨在提出一种新颖的深度学习框架SecEdge，旨在增强移动物联网环境中的实时网络安全。SecEdge框架集成了基于Transformer的模型以高效处理长距离依赖关系，以及用于对关系数据进行建模的图神经网络（GNN），并结合联邦学习以确保数据隐私并减少延迟。自适应学习机制不断更新模型参数以应对不断演变的网络威胁。该框架的性能在使用NSL-KDD、UNSW-NB15和CICIDS2017数据集的模拟环境中进行了评估。结果表明，SecEdge在NSL-KDD上对拒绝服务（DoS）攻击的检测率为98.8%，在UNSW-NB15上对中间人（MitM）攻击的检测率为98.5%，在CICIDS2017上对数据注入攻击的检测率为98.7%，优于现有方法。
