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ConvLSTM for Drone Intrusion Detection by Ali mazhar is a document available to read on EtoBox.
What is ConvLSTM for Drone Intrusion Detection about?
This research presents a novel ConvLSTM model for detecting intrusions in small drone communication networks, achieving an impressive accuracy of 99.99%. It evaluates various deep learning models using five realistic datasets to enhance UAV security against cyber threats. The study emphasizes the importance of robust cybersecurity measures and high-quality datasets in safeguarding the growing use of UAV technology across industries.
- Author
- Ali mazhar
- Language
- EN