Sky Guard: Machine Learning Based Intrusion Detection For Dynamic UAV Network Traffic
DOI:
https://doi.org/10.63856/ijis/v2i4/00036Keywords:
Secret Image Sharing (SIS), Steganography, Shamir’s Secret Sharing, Image Security, Cryptography, Data Privacy, Secure Image Transmission, Information Hiding.Abstract
The rapid growth of Unmanned Aerial Vehicles (UAVs) has expanded their use in areas like logistics, surveillance, agriculture, and disaster management. However, their integration with modern networks such as 5G raises significant security concerns due to vulnerabilities like spoofing, packet injection, and denial-of-service attacks. This paper proposes SkyGuard, a machine learning-based intrusion detection framework that analyzes UAV network traffic to identify malicious activities. It uses preprocessing, feature extraction, and Principal Component Analysis (PCA) for dimensionality reduction, followed by classification using multiple ML algorithms. Experimental results show that the system effectively detects attacks and enhances UAV network security, with strong performance measured through accuracy, precision, recall, and F1-score.
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