Layer-Level Security Enhancement in 5G Networks Using Deep Reinforcement Learning Techniques

Abstract

The advent of 5G technology has paved the way for unprecedented connectivity and efficiency in Internet of Things (IoT) networks. However, the inherent vulnerabilities in the physical layer of 5G pose significant security challenges. As 5G becomes the backbone of IoT ecosystems, the physical layer becomes susceptible to various security threats, including jamming attacks, signal interference, and unauthorized access. Traditional security measures fall short in dynamically adapting to these evolving threats, necessitating the exploration of innovative approaches such as DRL. Existing literature lacks a comprehensive exploration of applying DRL to secure the physical layer of 5G for IoT networks. This research seeks to bridge this gap by investigating the efficacy of DRL algorithms in autonomously enhancing security measures based on real-time threat assessments. This research aims to address these concerns by leveraging deep reinforcement learning (DRL) techniques to fortify the security of the physical layer in 5G-enabled IoT networks. The proposed methodology involves developing and training DRL agents to adaptively optimize physical layer parameters in response to potential security threats. Simulations will be conducted in a controlled environment to evaluate the performance and robustness of the DRL-based security framework. The chosen DRL algorithms will be fine-tuned to achieve optimal results in mitigating specific threats encountered in 5G IoT networks. The results include a demonstrable improvement in the security posture of the 5G physical layer, with the DRL-based approach effectively countering jamming attacks, mitigating interference, and proactively adapting to emerging threats. The findings of this research contribute valuable insights into the feasibility and effectiveness of integrating DRL into 5G IoT security frameworks.