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%.

本研究提出了一种新的用于无人机网络的入侵检测系统，采用二元元组表示来编码类别标签，结合基于深度学习的方法进行分类，通过捕捉复杂的类别关系和时序网络模式来增强入侵检测能力，并通过对不同无人机共同特征的交叉相关研究来排除可能误导入侵检测分类的相关特征。使用 UAV-IDS-2020 数据集进行了完整的实验研究，并使用不同的评估指标评估了所提出的入侵检测系统的性能，实验结果显示了所提出的多类别分类器模型的 95% 准确率。