Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach
نویسندگان
چکیده
This letter proposes a deep learning approach to detect change in the antenna orientation of transmitter or receiver as physical tamper attack OFDM systems using channel state information. We treat problem semi-supervised anomaly detection and utilize convolutional autoencoder (DCAE) tackle it. The past observations estimated information (CSI) are used train DCAE. Then, post-processing is deployed on trained DCAE output perform detection. Our experimental results show that proposed approach, an office hall environment, able average 99.6% events (TPR = 99.6%) while creating zero false alarms (FPR 0%).
منابع مشابه
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ژورنال
عنوان ژورنال: IEEE Wireless Communications Letters
سال: 2021
ISSN: ['2162-2337', '2162-2345']
DOI: https://doi.org/10.1109/lwc.2021.3072937