Reactive injection attacks are a class of security threats in wireless networks wherein adversaries opportunistically inject spoofing packets in the frequency band of a client thereby forcing the base-station to deploy impersonation-detection methods. Towards circumventing such threats, we implement secret-key based physical-layer signalling methods at the clients which allow the base-stations to deploy machine learning (ML) models on their in-phase and quadrature samples at the baseband for attack detection. Using Adalm Pluto based software defined radios to implement the secret-key based signalling methods, we show that robust ML models can be designed at the base-stations. However, we also point out that, in practice, insufficient availability of training datasets at the base-stations can make these methods ineffective. Thus, we use a federated learning framework in the backhaul network, wherein a group of base-stations that need to protect their clients against reactive injection threats collaborate to refine their ML models by ensuring privacy on their datasets. Using a network of XBee devices to implement the backhaul network, experimental results on our federated learning setup shows significant enhancements in the detection accuracy, thus presenting wireless security as an excellent use-case for federated learning in 6G networks and beyond.

翻译：反应式注入攻击是无线网络中的一类安全威胁，攻击者伺机在客户端频带内注入伪造数据包，迫使基站部署身份冒充检测方法。为应对此类威胁，我们在客户端实现了基于密钥的物理层信号处理方法，使基站能够对其基带同相正交样本部署机器学习模型进行攻击检测。通过使用Adalm Pluto软件定义无线电实现基于密钥的信号处理方法，我们证明可以在基站设计出鲁棒的机器学习模型。然而，我们同时指出，在实际中基站训练数据集不足可能导致这些方法失效。因此，我们在回程网络中采用联邦学习框架，由需要保护客户端免受反应式注入威胁的基站群组通过确保数据集隐私来协作优化其机器学习模型。利用XBee设备网络搭建回程网络的实验结果表明，我们的联邦学习设置显著提升了检测精度，从而将无线安全呈现为6G及未来网络中联邦学习的一个优秀应用场景。