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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    Title :

    A Hybrid Deep Learning Model for Intrusion Detection in Aerospace Vehicles


    Contributors:


    Publication date :

    2024-07-22


    Size :

    1072294 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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