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

TL;DR

This paper introduces a deep learning method using convolutional autoencoders to detect physical tamper attacks in OFDM systems by analyzing channel state information, achieving high detection accuracy with zero false alarms.

Contribution

It presents a novel semi-supervised deep learning approach for physical tamper detection in OFDM systems using CSI and autoencoders, with proven high accuracy.

Findings

Detects 99.6% of tamper events

Zero false alarms in experiments

Effective in office and hall environments

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%).