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 deep convolutional autoencoder (DCAE) to tackle it.
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.
DCAE, letter, antenna orientation of transmitter, channel state information, receiver, change, OFDM systems, semi-supervised anomaly detection problem, deep learning approach, deep convolutional autoencoder, physical tamper attack problem
