Journal
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume 10, Issue 7, Pages 1503-1507Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2021.3072937
Keywords
Receivers; Radio transmitters; Training; Convolution; Wireless networks; Decoding; Channel state information; OFDM; channel state information; deep learning; deep convolutional autoencoder; physical tamper attack
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Funding
- SCOTT Project, Johannes Kepler University, Linz Institute of Technology (LIT) [LIT-2016-1-SEE-026]
- Electronic Component Systems for European Leadership Joint Undertaking [737422, 876038]
- European Union
- InSecTT Project
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This study introduces a deep learning approach to detect changes in antenna orientation in OFDM systems, achieving a 99.6% detection rate for tamper events and zero false alarms in office and hall environments.
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%).
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