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%.

翻译：无人机（UAVs）在各种应用中日渐普及，尤其是在6G系统与网络兴起的背景下。然而，其广泛部署也引发了对其安全漏洞的担忧，这使得开发可靠的入侵检测系统（IDS）对于保障无人机安全和任务成功至关重要。本文提出了一种面向无人机网络的新型IDS。该系统采用二元组表示对类别标签进行编码，并利用基于深度学习的方法进行分类。所提出的系统通过捕获复杂的类别关系与时间性网络模式，增强了入侵检测能力。此外，本文还对不同无人机的常见特征进行了互相关性研究，以剔除可能误导所提IDS分类的相关特征。完整研究基于UAV-IDS-2020数据集进行，并采用多种评估指标对所提IDS的性能进行了评估。实验结果表明，所提出的多分类器模型具有95%的准确率，凸显了其有效性。