Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach | IEEE Journals & Magazine | IEEE Xplore

Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach


Abstract:

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 c...Show More

Abstract:

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%).
Published in: IEEE Wireless Communications Letters ( Volume: 10, Issue: 7, July 2021)
Page(s): 1503 - 1507
Date of Publication: 13 April 2021

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