  Unmanned Aerial Vehicles (UAVs) have become increasingly popular in various
applications, especially with the emergence of 6G systems and networks.
However, their widespread adoption has also led to concerns regarding security
vulnerabilities, making the development of reliable intrusion detection systems
(IDS) essential for ensuring UAVs safety and mission success. This paper
presents a new IDS for UAV networks. A binary-tuple representation was used for
encoding class labels, along with a deep learning-based approach employed for
classification. The proposed system enhances the intrusion detection by
capturing complex class relationships and temporal network patterns. Moreover,
a cross-correlation study between common features of different UAVs was
conducted to discard correlated features that might mislead the classification
of the proposed IDS. The full study was carried out using the UAV-IDS-2020
dataset, and we assessed the performance of the proposed IDS using different
evaluation metrics. The experimental results highlighted the effectiveness of
the proposed multiclass classifier model with an accuracy of 95%.
