In today’s linked world, aircraft vehicles need advanced communication technologies to operate. However, this dependency makes them susceptible to cyber dangers such intrusions into communication networks. In this research, we develop a hybrid deep learning model that enhances aerospace vehicle Intrusion Detection Systems (IDS). Our cascading LSTM and GRU network model handles time-series data well, solving MIL-STD-1553 communication traffic issues. Quantitative analyses surpass machine learning in detection metrics. The model can correctly detect complex infiltration attempts with few false negatives, with accuracy and recall of 99.33% and 99.17%, respectively.


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    Titel :

    A Hybrid Deep Learning Model for Intrusion Detection in Aerospace Vehicles


    Beteiligte:
    Gaurav, Akshat (Autor:in) / Gupta, Brij B. (Autor:in) / Tai Chui, Kwok (Autor:in)


    Erscheinungsdatum :

    22.07.2024


    Format / Umfang :

    1072294 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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