DL-Based Physical Tamper Attack Detection in OFDM Systems with Multiple Receiver Antennas: A Performance–Complexity Trade-Off

This paper proposes two deep-learning (DL)-based approaches to a physical tamper attack detection problem in orthogonal frequency division multiplexing (OFDM) systems with multiple receiver antennas based on channel state information (CSI) estimates. The physical tamper attack is considered as the unwanted change of antenna orientation at the transmitter or receiver. Approaching the tamper attack scenario as a semi-supervised anomaly detection problem, the algorithms are trained solely based on tamper-attack-free measurements, while operating in general scenarios that may include physical tamper attacks. Two major challenges in the algorithm design are environmental changes, e.g., moving persons, that are not due to an attack and evaluating the trade-off between detection performance and complexity. Our experimental results from two different environments, comprising an office and a hall, show the proper detection performances of the proposed methods with different complexity levels. The optimal proposed method achieves a 93.32% true positive rate and a 10% false positive rate with a suitable level of complexity.