Dynamic fuzz testing of UAV configuration parameters based on dual guidance of fitness and coverage

Abstract

ArduCopter's configuration parameter verification defects may cause the Unmanned Aerial Vehicle (UAV) in abnormal status. However, traditional UAV configuration parameter defect detection methods based on fuzz testing lack guidance design and inadequately detect configuration parameter defects. This paper proposes an improved configuration security defect analysis method based on fuzz testing. Using the fitness feedback mechanism based on the CAG neural network to guide the generation of fuzz testing cases, and using multiple coverage feedback mechanisms to guide the exploration direction of fuzz testing. Experimental results show that this method almost covers ArduCopter's position and attitude controller, guiding the UAV into abnormal states such as spin and crash, and detecting specific instances of configuration parameter defects.

KEYWORDS:

• Configuration defects
• fuzz testing
• fitness
• coverage
• feedback

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Key Research and Development Program of China [grant number: 2020YFB1005704].