Enhancing Network-edge Connectivity and Computation Security in Drone Video Analytics
Enhancing Network-edge Connectivity and Computation Security in Drone Video Analytics
复制标题
DOI：
10.1109/aipr50011.2020.9425341
复制
发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Alicia Esquivel Morel;Deniz Kavzak Ufuktepe;Robert Ignatowicz;Alexander Riddle;Chengyi Qu;P. Calyam;K. Palaniappan
中科院分区：
文献类型：
--
作者：
Alicia Esquivel Morel;Deniz Kavzak Ufuktepe;Robert Ignatowicz;Alexander Riddle;Chengyi Qu;P. Calyam;K. Palaniappan
带有高分辨率摄像机的无人机系统被用于航空成像、搜救和精准农业等许多作业。在飞行自组织网络(FANETS)中运行的多无人机系统本质上是不安全的，需要有效的安全方案来防御网络攻击，例如中间人攻击、重播攻击和拒绝服务攻击。在本文中，我们提出了一个基于云的端到端安全框架，即“DroneNet-SEC”，它为无人机视频分析提供安全的网络边缘连接和计算安全，以防御无人机系统中常见的攻击载体。DroneNet-SEC具有动态安全方案，使用机器学习来检测异常事件，并采取对策来应对集装箱视频分析任务的计算安全。该安全方案包括使用MAVLink协议设计的定制安全分组，以确保数据的私密性和完整性，而不会在实时FANET部署中导致性能严重下降。通过开源网络仿真器(NS-3)和移动无线网络研究平台(POWER)对DroneNet-SEC进行了协同仿真和仿真。我们在整体混合测试床上的性能评估实验表明，DroneNet-SEC成功地检测到学习的异常事件，并以轻量级的方式有效地保护了集装箱化任务的执行以及无人机视频分析中的通信。
Unmanned Aerial Vehicle (UAV) systems with high-resolution video cameras are used for many operations such as aerial imaging, search and rescue, and precision agriculture. Multi-drone systems operating in Flying Ad Hoc Networks (FANETS) are inherently insecure and require efficient security schemes to defend against cyber-attacks such as e.g., Man-in-the-middle, Replay and Denial of Service attacks. In this paper, we propose a cloud-based, end-to-end security framework viz., "DroneNet-Sec" that provides secure network-edge connectivity, and computation security for drone video analytics to defend against common attack vectors in UAV systems. The DroneNet-Sec features a dynamic security scheme that uses machine learning to detect anomaly events and adopts countermeasures for computation security of containerized video analytics tasks. The security scheme comprises of a custom secure packet designed with MAVLink protocol for ensuring data privacy and integrity, without high degradation of the performance in a real-time FANET deployment. We evaluate DroneNet-Sec in a hybrid testbed that synergies simulation and emulation via an open-source network simulator (NS-3) and a research platform for mobile wireless networks (POWDER). Our performance evaluation experiments in our holistic hybrid-testbed show that DroneNet-Sec successfully detects learned anomaly events and effectively protects containerized tasks execution as well as communication in drones video analytics in a light-weight manner.