As the positioning technology advances, the global navigation satellite system (GNSS) has been highly concerned in railway systems. Meanwhile, the attack surface to GNSS-enabled train positioning has expanded, and thus, the concerns regarding the positioning security have increased. To deal with potential threats from a GNSS spoofing attack, research on active detection is conducted. A framework of multi-domain-feature-based spoofing detection is established, under which the semi-supervised GANomaly network is designed to generate the data-driven model. Different blocks, including the multiscale group dilated convolution, the attention gate, and double-dilated temporal convolutional network residual block, are involved to improve the reconstruction ability of the vanilla GANomaly network. A dynamic threshold mechanism is adopted in identifying the existence of the spoofing attack. The experimental results with the open-source Texas Spoofing Test Battery dataset under the ds7 static scenario and a railway train dataset from the spoofing injection test demonstrate the certain advantages of the proposed solution over the reference single-feature-based methods and existing networks, which highlight the potential for achieving the resilient train positioning under the observing conditions with respect to the adopted datasets.
Multidomain Joint Spoofing Detection Based on a Semi-Supervised Detection Network for GNSS-Based Train Positioning
IEEE Transactions on Aerospace and Electronic Systems ; 61 , 2 ; 3936-3949
2025-04-01
11896129 byte
Article (Journal)
Electronic Resource
English
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