5G Slice Mutation to Overcome Distributed Denial of Service Attacks Using Reinforcement Learning

5G slices are susceptible to indirect Distributed Denial of Service (DDoS) attacks, where overwhelming traffic directed to one slice can also disrupt other slices sharing the same infrastructure Many current mitigation methods rely on a detection phase, which may not be effective against unknown or sophisticated attacks. Moving Target Defense (MTD) is a security mechanism that invalidates the adversary's collected information, and it can be deployed without the detection phase. In this paper, we propose a Slice Mutation technique based on Reinforcement Learning (SMRL) that reduces the impact of DDoS attacks on 5G slices while keeping the number of allocated slices acceptable. SMRL proposes a general RL model that considers ternary and ranking numbers to improve learning performance. We tested SMRL on computer networks attacked by a real botnet called Mirai and assessed its performance using various measures, including a new functionality analysis method The results indicate that SMRL decreases the number of slices impacted by a DDoS attack and enhances the distribution of slices among infrastructure resources by 46 % and 20 %, respectively.