Enhancing Security and Privacy in 5G Device-to-Device Communication: A Grey Wolf Optimization Algorithm Approach

Device-to-Device (D2D) communication has emerged as a key technology in 5G networks, enabling direct data exchange between devices with reduced latency and improved spectral efficiency. However, the decentralized nature of D2D communication introduces significant security and privacy challenges, including interference, jamming, and unauthorized access. Conventional approaches, such as the Gale–Shapley algorithm, primarily focus on stable pairing and fail to optimize overall system performance in dynamic and adversarial environments. To overcome these limitations, this paper proposes a secure and efficient framework based on Grey Wolf Optimization (GWO) for optimal transmitter–receiver association in 5G D2D networks. The proposed approach formulates the pairing problem as a multi-objective optimization task, incorporating key parameters such as Signal-to-Interference-plus-Noise Ratio (SINR), transmission distance, energy efficiency, and jamming effects. Furthermore, an AI-based threat detection mechanism is integrated to enable real-time identification and mitigation of potential security threats. Simulation results demonstrate that the proposed GWO-based framework significantly improves throughput, energy efficiency, and overall network security compared to conventional methods. Additionally, the inclusion of privacy-preserving key generation enhances secure data transmission, making the proposed system suitable for next-generation wireless communication environments.