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.
A Hybrid Deep Learning Model for Intrusion Detection in Aerospace Vehicles
22.07.2024
1072294 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch