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Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery

Deemah H. Tashman, Soumaya Cherkaoui

Abstract

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.

Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery

Abstract

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.

Paper Structure

This paper contains 20 sections, 12 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Architecture and Slicing Model
  4. Downlink Data Rate and QoE Constraints
  5. Resource Allocation Constraints
  6. SLA Formulation and Evaluation Metrics
  7. Served-Ratio SLA
  8. Average Rate SLA
  9. SLA Satisfaction Indicator
  10. Problem Formulation and Proposed DRL-based Solution
  11. Problem Formulation
  12. Deep Reinforcement Learning-based Solution
  13. Deep Reinforcement Learning--Based Slice-Level Attack
  14. Attack Surface
  15. Limited Jamming Capability
  16. ...and 5 more sections

Figures (4)

  • Figure 1: The system model.
  • Figure 2: Slice-level average SLA violation rate under no attack and DRL-based adversarial attack
  • Figure 3: Post-attack SLA recovery behavior per network slice under an RL-based adversarial attack.
  • Figure 4: Post-attack recovery of the average reward per slot under an RL-based adversarial attack.