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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Vydané v:	IEEE wireless communications letters Ročník 10; číslo 7; s. 1503 - 1507
Hlavní autori:	Dehmollaian, Eshagh, Etzlinger, Bernhard, Torres, Nuria Ballber, Springer, Andreas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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