Deep Reinforcement Learning-Based Moving Target Defense Approach to Secure Network Slicing in 5G and Beyond

Network slicing security in 5G and beyond 5G (B5G) networks is critical due to the wide range of supported services and applications. Existing literature focuses on reactive AI-based security that can detect and respond to threats after occurrence. In contrast, proactive security solutions, such as moving target defense (MTD), possess great promise. MTD involves constantly altering system configurations to increase uncertainty for attackers. Despite its potential, existing work that incorporates MTD often overlooks the intricate balance between enhancing security and maintaining network operational effi-ciency. This work proposes a novel approach to integrating Deep Reinforcement Learning (DRL) with MTD for network slicing security, our approach creates a moving target by dynamically reconfiguring IP addresses, complicating reconnaissance efforts, and thwarting potential attacks. Experimental results show that our solution achieves approximately 98 % effectiveness against Distributed Denial of Service (DDoS) attacks, demonstrating its efficacy in proactively mitigating threats.