Jamming Attacks on NextG Radio Access Network Slicing with Reinforcement Learning
Jamming Attacks on NextG Radio Access Network Slicing with Reinforcement Learning
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DOI：
10.1109/fnwf55208.2022.00076
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Yi Shi;Y. Sagduyu;T. Erpek;M. C. Gursoy
中科院分区：
文献类型：
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作者：
Yi Shi;Y. Sagduyu;T. Erpek;M. C. Gursoy
研究了在下一代无线接入网(RAN)中如何对网络分片的强化学习进行攻击。采用对抗性机器学习方法构建空中攻击，操控强化学习算法，扰乱下一代RAN切片的资源分配。资源块由基站(GNodeB)分配给用户设备的请求，并应用强化学习来最大化随时间推移接受的请求的总回报。同时，干扰机通过观察频谱，用自己的强化学习算法建立自己的代理模型。该代理模型用于选择在能量预算的约束下阻塞哪些资源块。干扰器的目标是最大化失败的网络切片请求的数量。为此，干扰器阻塞资源块，减少强化学习算法的奖励，作为更新网络切片强化学习算法的输入。因此，即使在干扰器停止干扰之后，网络分片性能也在一段时间内不能恢复。对这次攻击的恢复时间和奖励损失进行了评估。结果表明，与基准(随机和近视)干扰攻击相比，该攻击是有效的，并表明了NextG RAN切片对智能干扰器的脆弱性。
This paper studies how to launch an attack on reinforcement learning for network slicing in NextG radio access network (RAN). An adversarial machine learning approach is pursued to construct an over-the-air attack that manipulates the reinforcement learning algorithm and disrupts resource allocation of NextG RAN slicing. Resource blocks are allocated by the base station (gNodeB) to the requests of user equipments and reinforcement learning is applied to maximize the total reward of accepted requests over time. In the meantime, the jammer builds its surrogate model with its own reinforcement learning algorithm by observing the spectrum. This surrogate model is used to select which resource blocks to jam subject to an energy budget. The jammer's goal is to maximize the number of failed network slicing requests. For that purpose, the jammer jams the resource blocks and reduces the reinforcement learning algorithm's reward that is used as the input to update the reinforcement learning algorithm for network slicing. As result, the network slicing performance does not recover for a while even after the jammer stops jamming. The recovery time and the loss in the reward are evaluated for this attack. Results demonstrate the effectiveness of this attack compared to benchmark (random and myopic) jamming attacks, and indicate vulnerabilities of NextG RAN slicing to smart jammers.