Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery
This addresses security vulnerabilities in AI-managed cellular networks, which is an incremental but important domain-specific problem for network operators and users.
The paper studied adversarial attacks on AI-driven RAN slicing, showing that budget-constrained jamming causes severe, slice-dependent SLA violations and requires a non-negligible recovery period for DRL agents to return to baseline performance.
Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and large-scale Internet of Things deployments. A key enabling mechanism is radio access network (RAN) slicing, which dynamically partitions radio resources into virtual resource blocks to efficiently serve heterogeneous traffic classes, including enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). In this paper, we study the impact of adversarial attacks on AI-driven RAN slicing decisions, where a budget-constrained adversary selectively jams slice transmissions to bias deep reinforcement learning (DRL)-based resource allocation, and quantify the resulting service level agreement (SLA) violations and post-attack recovery behavior. Our results indicate that budget-constrained adversarial jamming can induce severe and slice-dependent steady-state SLA violations. Moreover, the DRL agent's reward converges toward the clean baseline only after a non-negligible recovery period.