Quantum inspired hyperparameter optimization for enhanced deep learning based intrusion detection in wireless sensor networks

Wireless Sensor Networks (WSNs) are increasingly threatened by evolving denial-of-service (DoS) attacks that degrade communication reliability and energy efficiency. Existing intrusion detection systems (IDS) that employ deep learning models often struggle with suboptimal hyperparameter tuning, slow convergence, and poor generalization under dynamic WSN conditions. To address these limitations, this study proposes a quantum search-enhanced bat algorithm (QS-BAT) that integrates quantum-inspired search dynamics with adaptive swarm intelligence for efficient and precise hyperparameter optimization. Five deep learning architectures—CNN, GAN, GRU, LSTM, and Transformer— were optimized and evaluated on the WSN-DS dataset under multiple attack scenarios. Experimental results demonstrate that the QS-BAT-optimized Transformer achieves 95.8 % accuracy and 95.7 % F1-score, significantly outperforming Grid Search, Bat Algorithm, Cuckoo Search, and Sparrow Search Algorithm. The novelty of this study lies in coupling the quantum exponential search with adaptive elite selection, enabling faster convergence and superior IDS performance in energy-constrained WSN environments.