Title
Control Parameters Considered Harmful: Detecting Range Specification Bugs in Drone Configuration Modules via Learning-Guided Search
Abstract
In order to support a variety of missions and deal with different flight environments, drone control programs typically provide configurable control parameters. However, such a flexibility introduces vulnerabilities. One such vulnerability, referred to as range specification bugs, has been recently identified. The vulnerability originates from the fact that even though each individual parameter receives a value in the recommended value range, certain combinations of parameter values may affect the drone physical stability. In this paper, we develop a novel learning-guided search system to find such combinations, that we refer to as incorrect configurations. Our system applies metaheuristic search algorithms mutating configurations to detect the configuration parameters that have values driving the drone to unstable physical states. To guide the mutations, our system leverages a machine learning based predictor as the fitness evaluator. Finally, by utilizing multi-objective optimization, our system returns the feasible ranges based on the mutation search results. Because in our system the mutations are guided by a predictor, evaluating the parameter configurations does not require realistic/simulation executions. Therefore, our system supports a comprehensive and yet efficient detection of incorrect configurations. We have carried out an experimental evaluation of our system. The evaluation results show that the system successfully reports potentially incorrect configurations, of which over 85% lead to actual unstable physical states.
Year
DOI
Venue
2022
10.1145/3510003.3510084
2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)
Keywords
DocType
ISSN
Drone security,configuration test,range specification bug,deep learning approximation
Conference
0270-5257
ISBN
Citations 
PageRank 
978-1-6654-9589-9
0
0.34
References 
Authors
12
7
Name
Order
Citations
PageRank
Ruidong Han100.34
Chao Yang28722.49
Siqi Ma300.68
JiangFeng Ma400.34
Cong Sun500.34
Juanru Li617924.07
Elisa Bertino7140252128.50