This letter proposes a deep learning approach to detect a change in the antenna orientation of transmitter or receiver as a physical tamper attack in OFDM systems using channel state information. We treat the physical tamper attack problem as a semi-supervised anomaly detection problem and utilize a deep convolutional autoencoder (DCAE) to tackle it. The past observations of the estimated channel state information (CSI) are used to train the DCAE. Then, a post-processing is deployed on the trained DCAE output to perform the physical tamper detection. Our experimental results show that the proposed approach, deployed in an office and a hall environment, is able to detect on average 99.6% of tamper events (TPR = 99.6%) while creating zero false alarms (FPR = 0%).

中文翻译：

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在 OFDM 系统中使用信道状态信息进行物理篡改攻击检测：一种深度学习方法

这封信提出了一种深度学习方法，用于检测发射机或接收机天线方向的变化，作为使用信道状态信息的 OFDM 系统中的物理篡改攻击。我们将物理篡改攻击问题视为半监督异常检测问题，并利用深度卷积自编码器 (DCAE) 来解决它。估计信道状态信息 (CSI) 的过去观察用于训练 DCAE。然后，在经过训练的 DCAE 输出上部署后处理以执行物理篡改检测。我们的实验结果表明，所提出的方法部署在办公室和大厅环境中，能够平均检测 99.6% 的篡改事件 (TPR = 99.6%)，同时产生零误报 (FPR = 0%)。