Experimental validation demonstrates high cyber threat detection accuracy in IoT edge computing, indicating enhanced defense mechanisms against network attacks.
The rapid increase in IoT devices has forecasted a massive volume of data across diverse environments. The Intelligent Search and Learner Optimization-based Quantum Deep Memory Neural Network (InSLO-QDMNet) model is proposed in this research to efficiently detect the cyber threats of IoT environments occurring at the edge computing layer. Ultimately, the proposed method effectively performs intricate computations and allows data to be mapped into higher-dimensional spaces, resulting in improved detection accuracy. Experimental validation demonstrates that the proposed method achieves exceptional results, gaining 98.01% accuracy, and error rate of 0.018, for evaluation using 90% of training data on IoT-Botnet dataset.
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Mohammed et al. (2026) studied this question.
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