Dehmollaian Eshagh, Etzlinger Bernhard, Torres Núria Ballber, Springer Andreas
JKU LIT SAL eSPML Lab, Institute for Communications Engineering and RF-Systems, Johannes Kepler University, 4040 Linz, Austria.
Institute for Communications Engineering and RF-Systems, Johannes Kepler University, 4040 Linz, Austria.
Sensors (Basel). 2022 Aug 30;22(17):6547. doi: 10.3390/s22176547.
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.
本文提出了两种基于深度学习（DL）的方法，用于在基于信道状态信息（CSI）估计的具有多个接收天线的正交频分复用（OFDM）系统中解决物理篡改攻击检测问题。物理篡改攻击被视为发射机或接收机处天线方向的不必要改变。将篡改攻击场景视为半监督异常检测问题，算法仅基于无篡改攻击的测量进行训练，同时在可能包括物理篡改攻击的一般场景中运行。算法设计中的两个主要挑战是环境变化，例如移动的人员，这并非由攻击引起，以及评估检测性能与复杂度之间的权衡。我们在包括办公室和大厅的两种不同环境中的实验结果表明，所提出的方法在不同复杂度水平下具有适当的检测性能。所提出的最优方法在适当的复杂度水平下实现了93.32%的真阳性率和10%的假阳性率。
