摘要
作为信息物理系统(Cyber-Physical Systems,CPS)的典型设备之一,无人机使用方便、对作业环境要求低、灵活性强,已广泛应用于农业、工业、军事等领域.其中,飞行控制系统是无人机核心基础服务,保障无人机遥测感知、通信覆盖、测绘救灾等应用的有效执行.但多变的物理环境、复杂的功能结构使无人机飞行控制系统在开发过程中容易引入各类软件安全问题,导致无人机发生劫持、坠毁、失控等严重问题.如何检测无人机飞控软件系统的安全问题变得非常重要.现有的大多数无人机异常检测技术依靠数字世界构造输入,难以及时发现无人机逻辑安全的问题,本文提出一种支持物理交互的无人机飞控软件安全检测方法,将静态与动态分析方法相结合,用模糊测试方法对无人机飞行控制软件的安全性进行测试,结果表明该方法能够以97%的高覆盖率对无人机飞控任务进行安全检测,并根据测试结果进行无人机特征数据提取,基于该特征数据采用机器学习的方法训练出双重异常检测模型,在多组数据集上与现有检测方法进行对比,本文方法达到发现无人机异常状况97.5%的准确率,有效检测出无人机飞控软件系统中的已知安全问题.
As one of the typical equipment of cyber-physical systems(CPS),UAVs are easy to use,have low requirements for the working environment and strong flexibility,and have been widely used in agriculture,industry,military and other fields.Among them,the flight control system is the core basic service of UAV,which ensures the effective implementation of UAV telemetry perception,communication coverage,surveying,mapping and disaster relief applications.However,the changeable physical environment and complex functional structure make it easy to introduce various software security problems in the development process of the UAV flight control system,resulting in serious problems such as hijacking,crashing,and loss of control of the UAV.How to detect the security of the UAV flight control software system has become very important.Most of the existing UAV anomaly detection technologies rely on the input of digital world construction,and it is difficult to find the problem of UAV logic security in time,so this paper proposes a security detection method for UAV flight control software that supports physical interaction,combines static and dynamic analysis methods,and combines fuzzing testing methods to test the security of UAV flight control software,the results show that the method can detect the safety of UAV flight control tasks with a high coverage rate of 97%,and extract UAV feature data according to the test resultsBased on the feature data,the machine learning method is used to train a double anomaly detection model,and by comparing with the existing detection methods on multiple datasets,the proposed method finds the abnormal condition of the UAV with an accuracy rate of 97.5%,and effectively detects the known safety problems in the UAV flight control software system.
作者
习宁
周晓琳
孙聪
李乔杨
马建峰
郭鑫玉
XI Ning;ZHOU Xiao-lin;SUN Cong;LI Qiao-yang;MA Jian-feng;GUO Xin-yu(School of Cyber Engineering,Xidian University,Xi’an,Shaanxi 710071,China;AVIC Xi’an Aeronautics Computing Technique Research Institute,Xi’an,Shaanxi 710065,China)
出处
《电子学报》
北大核心
2025年第3期765-781,共17页
Acta Electronica Sinica
基金
国家自然科学基金(No.92267204,No.62232013,No.U24A20243)。
关键词
无人机飞控软件安全
模糊测试
异常检测
安全检测
机器学习
unmanned aerial vehicle flight control
fuzzing
software security
anomaly detection
security testing
