Model-based GNSS spoofing detection using a hybrid convolutional autoencoder method

The spoofing attack brings more serious threats and challenges to the Global Navigation Satellite System (GNSS) receiver. The rapid and accurate spoofing detection mechanism is of great significance to the credibility and security of GNSS-enabled transport applications. In this paper, an unsupervised classification solution is proposed to detect GNSS spoofing by analyzing the features of Coarse Acquisition (C/A) code Autocorrelation Function (ACF) using a Hybrid Convolutional Autoencoder (HCAE) method integrated with an attention-driven memory network. A dynamic threshold-based protection mechanism is introduced to reduce the system's sensitivity to unexpected anomalies, thereby enhancing detection accuracy. The effectiveness of the proposed solution is verified by comparison with referencing detection methods using the Texas Spoofing Test Battery (TEXBAT) and spoofing injection test datasets. Specifically, the performance indices of the proposed method are improved over the involved referencing methods, which demonstrate that this solution can realize accurate and efficient detection of GNSS spoofing under the data-driven scheme.