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Human Augmentation of UAV Cyber-Attack Detection

机译：人类增强无人网络攻击检测

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摘要

Unmanned aerial vehicles (UAVs) have extensive applications in both civilian and military applications. Nevertheless, the continued development of UAVs has been accompanied by security concerns. UAV navigation systems are potentially vulnerable to malicious attacks that target their Global Positioning System (GPS). Thus, efficient GPS hacking detection with high success rate is paramount. Significant effort has been put into developing autonomous hacking detection techniques. However, little research has considered how a human operator can contribute to the security of such systems. In this paper, we propose a human-autonomy collaborative approach for a single operator of multiple-UAV supervisory control systems, where human geo-location is used to help detect possible UAV cyber-attacks. An experiment was designed and conducted using the RESCHU-SA experiment platform to evaluate this approach. The primary results show that 65% of all experiment sessions reached over 80% success rate in UAV hacking detection, while only 17% of participants lost one or more UAVs because of incorrect hacking detections. These results suggest that such an approach could help achieve better security guarantees for human-in-the-loop autonomous UAV systems that are prone to cyber-attacks.
机译：无人驾驶飞行器（无人机）在民用和军事应用中具有广泛的应用。尽管如此，无人机的持续发展已经伴随着安全问题。 UAV导航系统可能易于针对其全球定位系统（GPS）的恶意攻击。因此，具有高成功率的高效GPS黑客检测是至关重要的。重大努力开发了自主黑客检测技术。然而，很少的研究考虑了人类运营商如何促进这种系统的安全性。在本文中，我们提出了一种用于多UAV监控系统的单个运营商的人类自主协作方法，人类地理位置用于帮助检测可能的无人网络攻击。使用Reschu-SA实验平台设计并进行实验，以评估这种方法。主要结果表明，65％的所有实验会议在无人机攻击检测中达到80％以上的成功率，而只有17％的参与者由于黑客检测不正确地丢失了一个或多个无人机。这些结果表明，这种方法可以帮助实现更好的循环自主UAV系统的安全保障，这些无人机系统易于网络攻击。

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