论文标题
在OFDM系统中使用频道状态信息进行物理篡改攻击检测：一种深度学习方法
Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach
论文作者
论文摘要
这封信提出了一种深度学习方法，以检测发射机或接收器的天线方向的变化，作为使用频道状态信息在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的异常检测问题，并利用深层卷积自动编码器（DCAE）来解决它。过去对估计的通道状态信息（CSI）的观察结果用于训练DCAE。然后，将后处理部署在训练有素的DCAE输出上，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，能够平均检测到篡改事件的99.6％（TPR = 99.6％），同时创建零错误警报（FPR = 0％）。
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%).
