自主无人机的上下文感知运行安全

中文摘要 AI 辅助

基于自主无人机的服务因其机动性、灵活性、成本效益以及集成各种传感器和执行器的能力，在各个应用领域引起了广泛关注。然而，针对无人机系统的运行故障或网络攻击可能导致严重的经济影响和安全隐患。因此，确保自主无人机运行的安全性和可靠性对于无人机赋能服务的安全部署至关重要。然而，由于无人机有限的计算资源和功率预算（电池），以及无人机运行因移动性而表现出的时间和顺序行为，传统安全措施显得不足。在本文中，我们通过利用循环神经网络（RNN），特别是专注于长短期记忆（LSTM）网络，来解决这一差距，用于自主无人机运行的安全性和可靠性，因为其具有时间和顺序分析能力。我们利用这些能力对自主无人机传感器数据和运行命令进行异常检测，我们称之为使用拒绝检测引擎IDS（DUDE-IDS）。我们将所提出的DUDE-IDS集成到无人机任务计算机中（即直接在无人机上运行，而不是在边缘节点或地面控制站上），以监控数据流并实时检测潜在威胁。广泛的实验结果表明，该方法在识别与GPS欺骗、中间人（MITM）、重放和拒绝服务（DoS）攻击相关的异常方面具有有效性，准确率达到98%。我们还评估了不同配置下的资源利用率和功耗，证明了我们的方法在实时主动无人机运行中的适用性。

英文摘要

Autonomous drone-based services have been gaining significant interest across various application domains due to their mobility, flexibility, cost-effectiveness, and capability to integrate various sensors and actuators. However, operational failures or cyberattacks targeting drone systems can lead to severe economic impacts and safety concerns. Hence, ensuring secure and reliable autonomous drone operations is essential for the safe deployment of drone-enabled services. Nevertheless, the traditional security measures fall short due to drones' limited computational resources and power budget (battery), as well as the temporal and sequential behavior of drone operations due to drones' mobile nature. In this paper, we address this gap by utilizing Recurrent Neural Networks (RNNs), specifically focusing on Long Short-Term Memory (LSTM) networks, for autonomous drone operation security and reliability due to their temporal and sequential analysis capabilities. We leverage these capabilities for anomaly detection in autonomous drone sensor data and operation commands, which we refer to as Denial of Usage Detection Engine IDS (DUDE-IDS). We integrated the proposed DUDE-IDS into the drone mission computer (i.e., operating directly on drones rather than an edge node or ground control stations) to monitor data flows and to detect potential threats in real-time. Extensive experimental results demonstrate the effectiveness of this approach in identifying anomalies associated with GPS spoofing, Man-in-the-Middle (MITM), replay, and Denial-of-Service (DoS) attacks with 98% accuracy. We also evaluate our resource utilization and power consumption under different configurations, demonstrating the applicability of our approach in active drone operations in real-time.

发表机构

• University of North Texas（北德克萨斯大学）

机构由 AI 辅助整理，请以论文原文为准。