A novel intrusion detection framework using hybrid deep learning to detect IIoT cloud environments attacks

The Internet of Things (IoT) presents considerable hurdles, especially in maintaining security across its swiftly proliferating applications. Administering security protocols and updating individual IoT devices to address emerging risks is resource intensive. Furthermore, the extensive data produced by IoT devices presents a significant opportunity for machine learning to improve threat detection. This study investigates the application of deep learning frameworks for the analysis of IoT network traffic to enhance intrusion detection and strengthen cybersecurity. A hybrid intrusion detection system (HIDS) is suggested, utilizing an iterative ensemble method that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for enhanced classification accuracy. Adaptive Synthetic Sampling (ADASYN) is utilized to rectify data imbalance, whereas Recursive Feature Elimination (RFE) enhances classifier efficacy by eliminating extraneous features. This comprehensive approach improves detection precision and system robustness against cyber-attacks. The proposed CNN–LSTM-based IDS was evaluated on five benchmark datasets. Across these datasets, the model consistently achieved high performance, including 98.89% accuracy on KDDCup99, 97% on CAN-BUS, 97% on NSL-KDD, and 99% on CICIDS, demonstrating its robustness across diverse IoT-Fog scenarios. Although increasing the model complexity yielded only marginal gains in detection performance, it substantially increased computational demands, indicating diminishing returns from more complex architectures. Customized deep learning approaches combined with effective preprocessing significantly improved IDS performance in IoT-Fog cybersecurity frameworks.