The spoofing attack would be a serious threat to location-based applications based on Global Navigation Satellite System (GNSS). To mitigate the negative effect of the spoofing attack that makes the GNSS receiver obtain fake and misleading positioning information, the identification of the spoofing attack is a significant step before the countermeasure is adopted. In this paper, considering constraints of existing methods, SpoofSpike network, which is a novel post-correlation solution is proposed using the Spiking Neural Network (SNN). This GNSS spoofing identification scheme is based on the differences between the practically measured Cross Ambiguity Functions (CAFs) and the predicted one in the GNSS receiver information processing. Under the overall solution architecture, details about the spiking neuron model and the SpoofSpike network are given. The decision-making mechanism to identify the spoofing attack is analyzed. Results from the test and comparisons using the TEXBAT datasets illustrate that the SpoofSpike network-based solution is capable of realizing effective identification according to the comparison of the spoofing score with the threshold, and it outperforms other SNN-based models and the Artificial Neural Network (ANN) counterpart.


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    Title :

    Post-correlation Identification of GNSS Spoofing based on Spiking Neural Network


    Contributors:
    Wang, Siqi (author) / Liu, Jiang (author) / Cai, Bai-Gen (author) / Lu, De-Biao (author)


    Publication date :

    2024-06-02


    Size :

    1551922 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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