Smart City Electronics Security Using XGBoost with Metaheuristic Algorithms

The rapid development of smart cities and the integration of IoT devices have significantly increased security vulnerabilities, especially within consumer electronics, exposing them to complex cyber-attacks. This paper aims to develop a robust security model to enhance threat detection and protection of these devices in smart city environments. We propose a novel approach utilizing the Harris Hawks Optimizer (HHO) for feature selection and the Mountain Gazelle Optimizer (MGO) for hyperparameter tuning within an XGBoost-based framework for network traffic analysis. This dual optimization technique is designed to balance computational efficiency with high accuracy, ensuring scalability across diverse smart city applications. Experimental evaluations on a labeled dataset indicate that our model outperforms conventional machine learning approaches, achieving 91.13% accuracy, 95.48% precision, and 91.13% recall, thereby reducing false positives while maintaining high detection rates. These results validate our model's potential as an effective and scalable solution for enhancing cybersecurity in interconnected urban systems, paving the way for further exploration of hybrid optimization strategies and broader datasets to support real-time smart city applications.