Title: Detection of malware in ground control stations of unmanned aerial vehicles based on image processing

Authors: Orkhan Valikhanli

Addresses: Institute of Information Technology, Baku, Azerbaijan

Abstract: Recently, unmanned aerial vehicles (UAVs) have become very popular due to their wide range of applications. UAVs are quite popular because they are more affordable and simpler to use compared to other vehicular systems. However, as with other cyber physical systems UAVs and their ground control stations (GCSs) may also be targeted by attackers. In this work, greyscale images are analysed to detect malwares in GCSs. The proposed hybrid model is based on ResNet-50 and support vector machine (SVM). ResNet-50 is used to extract necessary features from images. Subsequently, SVM is used to classify malware based on extracted features. Moreover, other hybrid models are also tested in this work to compare final results. As a result, proposed model achieved 98.62% accuracy.

Keywords: unmanned aerial vehicle; UAV; ground control station; GCS; image processing; cyberattack; malware.

DOI: 10.1504/IJICS.2025.145116

International Journal of Information and Computer Security, 2025 Vol.26 No.1/2, pp.147 - 158

Received: 04 Apr 2023
Accepted: 15 Aug 2024
Published online: 19 Mar 2025 *

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