Unmanned Aerial Vehicles (UAVs) are being used extensively in various fields. UAVs provide various services to users, including monitoring, logistics and sensing because of their flexible deployment and dynamic reconfigurability. However, UAV networks have become more susceptible to malicious threats because of their multi-connectivity and openness. A great effort has been made to develop an effective Intrusion Detection System (IDS) based on machine-learning approaches for UAVs. Unfortunately, existing methods were unable to identify real-time and zero-day attacks for UAV networks. This is due to that existing methods have still used obsolete datasets and past knowledge-based detection. Also, the shortcomings of standalone IDS render them unsuitable for defending UAV networks from potential security risks. Further, the lack of precise identification for compromised UAV nodes in UAV networks poses a critical security gap, risking the entire network’s integrity with the compromise of a single node. Therefore, in this work, we propose an autonomous collaborative IDS (UAV-CIDS) with a Feedforward Convolutional Neural Network (FFCNN), which accurately identifies zero-day with high accuracy. The proposed solution takes into account encoded Wi-Fi traffic logs of three popular UAVs types: DBPower UDI, Parrot Bebop and DJI Spark. Evaluation results indicate that our FFCNN model has produced outstanding results based on the UAVIDS dataset with 98.23% accuracy compared to existing models. After the detection of attacks, their mitigation is equally significant. In addition, we also design and implement real-time incident response handling against cyber-attacks on UAV Networks. The incident response handling will assist in minimizing the effects of a security breach, remediate vulnerabilities and systematically secure the entire UAV networks.

中文翻译：

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使用深度学习的无人机网络实时协作入侵检测系统


无人机（UAV）正在广泛应用于各个领域。无人机因其灵活的部署和动态可重构性而为用户提供各种服务，包括监控、物流和传感。然而，无人机网络由于其多连接性和开放性，变得更容易受到恶意威胁。人们付出了巨大的努力来开发基于无人机机器学习方法的有效入侵检测系统（IDS）。不幸的是，现有方法无法识别无人机网络的实时攻击和零日攻击。这是因为现有方法仍然使用过时的数据集和过去的基于知识的检测。此外，独立 IDS 的缺点使其不适合保护无人机网络免受潜在的安全风险。此外，无人机网络中受损的无人机节点缺乏精确识别，造成了严重的安全漏洞，单个节点的受损会危及整个网络的完整性。因此，在这项工作中，我们提出了一种带有前馈卷积神经网络（FFCNN）的自主协作IDS（UAV-CIDS），它可以高精度地识别零日漏洞。所提出的解决方案考虑了三种流行无人机类型的编码 Wi-Fi 流量日志：DBPower UDI、Parrot Bebop 和 DJI Spark。评估结果表明，我们的 FFCNN 模型基于 UAVIDS 数据集产生了出色的结果，与现有模型相比，准确率达到 98.23%。检测到攻击后，缓解措施同样重要。此外，我们还设计和实施针对无人机网络网络攻击的实时事件响应处理。 事件响应处理将有助于最大限度地减少安全漏洞的影响、修复漏洞并系统地保护整个无人机网络。