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%).
@article{arxiv.2011.03573,
title = {Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach},
author = {Eshagh Dehmollaian and Bernhard Etzlinger and Núria Ballber Torres and Andreas Springer},
journal= {arXiv preprint arXiv:2011.03573},
year = {2021}
}