Enhancing web of things security using Harris hawks optimization with reinforcement learning

• Proposed a novel Harris Hawks Optimization (HHO) based meta-learning framework to enhance Web of Things security using reinforcement learning • HHO-optimized Deep Q-Network achieved superior performance (98.54% accuracy) compared to baseline models on the CIC-IoT-2023 dataset • Demonstrated robust generalization across different attack scenarios through validation on both CIC-IoT-2023 and Bot-IoT datasets • Framework provides adaptive defense mechanisms against emerging cyber threats through efficient hyperparameter optimization • Statistical analysis confirms significant performance improvements over conventional DQN approaches (p < 0.05) The Web of Things (WoT) interconnects a rapidly growing population of smart devices and sensors, enabling innovative applications while exposing an ever‑expanding attack surface. Reinforcement learning (RL) can adaptively detect and mitigate such attacks, yet conventional RL struggles to converge in WoT’s high‑dimensional state‑action spaces. We address this limitation by augmenting RL with the Harris Hawks Optimization (HHO) algorithm. HHO is a recent meta‑heuristic optimization method that balances global exploration with local exploitation and is well suited to large search spaces. We propose an HHO‑based meta‑learning framework that aims to identify hyper‑parameters and network architecture for a deep‑Q network (DQN) defender, maximizing average episodic reward in simulated WoT environments. Experiments on the CIC‑IoT‑2023 and Bot‑IoT datasets show that an HHO‑optimized DQN converges faster and achieves higher accuracy than all tested baselines—including vanilla, double and dueling DQNs, PPO, A3C and Transformer-based agents—illustrating the promise of bio-inspired optimization for adaptive WoT security.