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

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...

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Veröffentlicht in:IEEE wireless communications letters Jg. 10; H. 7; S. 1503 - 1507
Hauptverfasser: Dehmollaian, Eshagh, Etzlinger, Bernhard, Torres, Nuria Ballber, Springer, Andreas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Anomalies
Channel state information
Convolution
Decoding
deep convolutional autoencoder
Deep learning
False alarms
OFDM
physical tamper attack
Post-processing
Radio transmitters
Receivers
State (computer science)
Training
Wireless networks
ISSN:2162-2337, 2162-2345
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Zusammenfassung: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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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2021.3072937