研究对抗攻击如何破坏AI驱动的无线网络切片，导致服务降级并影响恢复。
Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery
- 对手有限预算下干扰切片传输，误导AI资源分配决策
- 引发严重且依赖切片类型的长期服务协议违约
- AI模型需较长时间恢复，适合关注5G安全与可靠性研究者
下一代（NextG）蜂窝网络需支持沉浸式多媒体和大规模物联网等多样化数据速率与延迟需求。核心支撑技术是无线接入网（RAN）切片，将无线资源动态划分为虚拟资源块，以高效服务包括增强移动宽带（eMBB）、海量机器通信（mMTC）和超可靠低时延通信（URLLC）在内的异构业务。本文研究对抗攻击对基于AI的RAN切片决策的影响，其中预算受限的攻击者选择性干扰切片传输，以扭曲深度强化学习（DRL）驱动的资源分配，并量化由此产生的服务等级协议（SLA）违约情况及攻击后恢复行为。结果表明，预算受限的对抗性干扰可导致严重且依赖切片的稳态SLA违约；此外，DRL代理的奖励仅在非忽略的恢复期后才收敛至正常基准水平。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。