Introducing a hybrid intrusion detection method for IoT-cloud environments based on ResNeXt and improved Ebola optimization search algorithm

With the advent of IoT and its immense possibilities through cloud randomization, connectivity has matured almost out of proportion; yet this factor has equally opened various attack surfaces thereby rendering IoT-cloud environments vulnerable to multiple attacks. To address the issue, we introduce a versatile approach for hybrid intrusion diagnosis made possible through ResNeXt, a DCNN architecture noted for high efficiency and scalability in extracting features, and the Improved Ebola Optimization Search Algorithm (IEOSA), a novel metaheuristic optimizer fashioned after the spread dynamics of the Ebola virus, yet enhanced to offer speed and reliability in search performance. The proposed approach uses the comprehensive feature extraction ability of ResNeXt combined with the improved searching efficiency of the IEOSA to provide a superior method for the detection of anomalies and intrusions in an IoT-cloud environment. The network attained a detection accuracy of 98.3% and above 97% for recall, F1 score, and precision on standard datasets, including CICIDS 2017 and NSL-KDD. The hybrid framework enhances the performance of traditional and even a few deep-learning techniques, to provide a more secure IoT-cloud ecosystem. The results highlight the opportunity for integrating deep learning with a robust metaheuristic optimization approach for better efficiency and effectiveness in intrusion detection within digital infrastructures.