Abstract
The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats. Intrusion detection systems have become effective in monitoring network traffic and activities to identify anomalies that are indicative of attacks. However, constraints such as limited computing resources at fog nodes render conventional intrusion detection techniques impractical. This paper proposes a novel framework that integrates stacked autoencoders, CatBoost, and an optimised transformer-CNN-LSTM ensemble tailored for intrusion detection in fog and IoT networks. Autoencoders extract robust features from high-dimensional traffic data while reducing the dimensionality of the efficiency at fog nodes. CatBoost refines features through predictive selection. The ensemble model combines self-attention, convolutions, and recurrence for comprehensive traffic analysis in the cloud. Evaluations of the NSL-KDD, UNSW-NB15, and AWID benchmarks demonstrate an accuracy of over 99% in detecting threats across traditional, hybrid enterprises and wireless environments. Integrated edge preprocessing and cloud-based ensemble learning pipelines enable efficient and accurate anomaly detection. The results highlight the viability of securing real-world fog and the IoT infrastructure against continuously evolving cyber-attacks.
Publisher
Public Library of Science (PLoS)
Reference66 articles.
1. Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system;W. L. Al-Yaseen;Expert Syst Appl,2017
2. Fog Intelligence for Network Anomaly Detection;K. Yang;IEEE Netw,2020
3. N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders;Y. Meidan;IEEE Pervasive Comput,2018
4. S. Iftikhar et al., “AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions A R T I C L E I N F O AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions,” 2022.
5. Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study;M. A. Ferrag;Journal of Information Security and Applications,2020