A Hybrid Delay-aware Approach Towards UAV Flight Data Anomaly Detection
A Hybrid Delay-aware Approach Towards UAV Flight Data Anomaly Detection
复制标题
DOI：
10.1109/icnc57223.2023.10074138
复制
发表时间：
2023-02
期刊：
影响因子：
--
通讯作者：
Mengjie Jia;A. Raja;Jiawei Yuan
中科院分区：
文献类型：
--
作者：
Mengjie Jia;A. Raja;Jiawei Yuan
随着无人驾驶飞行器（UAV）技术的快速发展，无人机现在越来越多地用于执行军事和民用任务。同时，无人机作为一个复杂的网络物理系统，也面临着内部系统错误和外部网络攻击等多方面的安全可靠性问题。最近的研究主要集中在利用人工智能和机器学习技术来预测无人机的飞行状态，利用无人机的飞行数据进行异常检测。然而，这些方法往往忽略了无人机运行过程中状态变化期间存在的预测延迟，如果不适当处理，这不可避免地会导致假警报，并为恶意对手打开了一个窗口。本文提出了一种对无人机飞行数据进行有效异常检测和恢复的新方法。我们的方法采用混合设计，在保持异常检测的高可靠性的同时，消除了状态变化期间的误报。我们对从多个无人机飞行路径收集的飞行数据进行了评估。评价结果验证了混合设计的有效性，既实现了较高的异常检测精度，又实现了可靠的恢复。
With the rapid development of unmanned aerial vehicle (UAV) technologies, UAVs are now increasingly leveraged to perform military and civilian tasks today. Meanwhile, as a complex cyber-physical system, UAVs are also facing security and reliability concerns raised by internal systems errors and external cyber-attacks from multiple aspects. Recent research has spent efforts on leveraging AI and machine learning techniques to predict the flying status of UAVs using their flight data for anomaly detection. However, these methods often ignore the prediction delay existing in status-changing periods during the UAV’s operation, which inevitably causes false alarms and opens a window for malicious adversaries if they are not appropriately addressed. In this paper, we propose a new approach to enable effective anomaly detection and recovery for UAV flight data. Our approach adopts a hybrid design to eliminate false alarms during the status-changing periods while maintaining the high reliability of anomaly detection. We evaluate the proposed approach on flight data collected from multiple UAV flight paths. Our evaluation results validate the effectiveness of our hybrid design, which achieves both high anomaly detection accuracy and reliable recovery.