Jamming Attacks and Mitigation in Transfer Learning Enabled 5G RAN Slicing

Radio access technology is crucial in both 5G and 6G cellular networks, providing differentiated services that demand reliability, low latency, and high throughput. To meet these requirements, machine learning (ML) has demonstrated considerable progress by facilitating resource allocation. However, these ML techniques can be susceptible to attacks, and the jamming attack is one of the most considered attacks in the literature, disrupting network functionality by sending interference signals. This paper, to the best of our knowledge for the first time, examines the vulnerability of radio access networks (RANs) to jamming attacks on resource allocation of a transfer reinforcement learning (TRL) based system and provides a mitigation approach to such attacks. A system model is presented for RAN slicing, followed by an introduction of the TRL algorithm for resource allocation. Afterward, we investigate covert patterned jamming attack (CPJA) on the TRL algorithm in downlink communication which decreases system throughput by 17% and 38.14% in the expert and learner agents and increases latency by 7.36% and 9.37% respectively. In addition, we propose a neural network (NN) solution to mitigate the CPJA trained on the network side and provide the trained NN model to the users' equipment (UEs) to eliminate interference from the signal by the filter. The trained NN is applied to predict the future activity of the interference generated by the attacker. Attack mitigation reduces the impact of the attack while the system's throughput suffers a 6% and 1.8% degradation, and its latency increases by 6.5% and 3.83% compared to the original system for expert and learner agents, respectively.