Year: 2024
Type: conference-paper
Abstract: As UAVs have been widely used in various domains, such as the military and industry, their safety and security have become crucial. One of their root causes is software bugs, which fall into two bug categories: traditional software bugs, such as memory safety bugs, and UAV-specific logical model-misimplementation (LMM) bugs leading to physical misbehavior, such as crashes. To discover and localize bugs, many proactive and reactive techniques have been proposed. However, LMM bug mitigation techniques are still immature, unlike well-established techniques for traditional software bugs, because existing approaches are unable to track the causal relationship between the LMM bug root cause in software and its resulting physical misbehavior. Specifically, existing proactive approaches require extensive, time-consuming dynamic testing to capture the physical impacts of LMM bug exploitation amidst a vast input space. Conversely, previous reactive approaches are inaccurate because existing work cannot accurately identify the causal relationship between misbehavior and bug-triggering inputs mixed with benign but suspicious inputs.
Authors: Changyul Lee, Deokjin Kim, Giyeol Kim, Sangwook Lee, Taegyu Kim
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404034693