Integrating Deep Learning and Meta-Heuristics for Enhanced IoT Security and Energy Efficiency

Abstract

The increasing proliferation of Internet of Things (IoT) devices in critical sectors such as smart grids, healthcare, and industrial systems has led to unprecedented advancements; however, it also introduces significant security vulnerabilities and energy inefficiencies that cannot be addressed by conventional methods. To overcome these challenges, this paper presents a novel, integrated framework that synergizes deep learning (DL) techniques with advanced meta-heuristic optimization methods to enhance both IoT security and energy efficiency in a scalable manner.
At the core of our approach is a Deep Reinforcement Learning (DRL)-enabled intrusion detection system (IDS) that continuously monitors network traffic and dynamically adapts to emerging cyber threats. The DRL module learns optimal defense strategies through real-time interaction with the environment, significantly improving detection accuracy. Complementing this, we introduce a hybrid optimization strategy that combines the strengths of the Chimp Optimization Algorithm (ChOA) and Grey Wolf Optimization (GWO) to address the energy challenges inherent in batteryless IoT systems and smart grid communications. This dual-optimization process not only refines energy allocation strategies but also ensures that network operations remain robust and efficient under variable conditions. The proposed framework was validated through extensive simulations, where it demonstrated a 20% improvement in intrusion detection accuracy over traditional approaches and a 15% reduction in energy consumption compared to baseline energy management models. Two key novel contributions underpin this work: a five-dimensional meta-heuristic optimizer designed for enhanced feature selection, which significantly bolsters the decision-making capabilities of the DRL-based IDS, and a Quality of Service (QoS)-aware energy optimization model that effectively balances energy efficiency with network performance requirements. Overall, this study provides a comprehensive, scalable solution that addresses the dual challenges of security and energy efficiency in next-generation IoT ecosystems, paving the way for future research and real-world implementations that further minimize resource consumption while maintaining robust security postures.