Range specification bug detection in flight control systems through fuzzing

Developers and manufacturers provide configurable control parameters for flight control programs to support various environments and missions, along with suggested ranges for these parameters to ensure flight safety. However, this flexible mechanism can also introduce a vulnerability known as range...

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Main Authors: HAN, Ruidong, MA, Siqi, LI, Juanru, NEPAL, Surya, LO, David, MA, Zhuo, MA, Jianfeng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9263
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-102632024-09-02T04:48:03Z Range specification bug detection in flight control systems through fuzzing HAN, Ruidong MA, Siqi LI, Juanru NEPAL, Surya LO, David MA, Zhuo MA, Jianfeng Developers and manufacturers provide configurable control parameters for flight control programs to support various environments and missions, along with suggested ranges for these parameters to ensure flight safety. However, this flexible mechanism can also introduce a vulnerability known as range specification bugs. The vulnerability originates from the evidence that certain combinations of parameter values may affect the drone's physical stability even though its parameters are within the suggested range. The paper introduces a novel system called icsearcher, designed to identify incorrect configurations or unreasonable combinations of parameters and suggest more reasonable ranges for these parameters. icsearcher applies a metaheuristic search algorithm to find configurations with a high probability of driving the drone into unstable states. In particular, icsearcher adopts a machine learning-based predictor to assist the searcher in evaluating the fitness of configuration. Finally, leveraging searched incorrect configurations, icsearcher can summarize the feasible ranges through multi-objective optimization. icsearcher applies a predictor to guide the search, which eliminates the need for realistic/simulation executions when evaluating configurations and further promotes search efficiency. We have carried out experimental evaluations of icsearcher in different control programs. The evaluation results show that the system successfully reports potentially incorrect configurations, of which over 94%94% leads to unstable states. 2024-03-31T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/9263 info:doi/10.1109/TSE.2024.3354739 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Drones Aerospace Control Trajectory Computer Bugs Fuzzing Actuators Codes Drone Security Configuration Test Range Specification Bug Deep Learning Approximation Flight Control Flight Control System Control Programs Control Parameters Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Drones
Aerospace Control
Trajectory
Computer Bugs
Fuzzing
Actuators
Codes
Drone Security
Configuration Test
Range Specification Bug
Deep Learning Approximation
Flight Control
Flight Control System
Control Programs
Control Parameters
Software Engineering
spellingShingle Drones
Aerospace Control
Trajectory
Computer Bugs
Fuzzing
Actuators
Codes
Drone Security
Configuration Test
Range Specification Bug
Deep Learning Approximation
Flight Control
Flight Control System
Control Programs
Control Parameters
Software Engineering
HAN, Ruidong
MA, Siqi
LI, Juanru
NEPAL, Surya
LO, David
MA, Zhuo
MA, Jianfeng
Range specification bug detection in flight control systems through fuzzing
description Developers and manufacturers provide configurable control parameters for flight control programs to support various environments and missions, along with suggested ranges for these parameters to ensure flight safety. However, this flexible mechanism can also introduce a vulnerability known as range specification bugs. The vulnerability originates from the evidence that certain combinations of parameter values may affect the drone's physical stability even though its parameters are within the suggested range. The paper introduces a novel system called icsearcher, designed to identify incorrect configurations or unreasonable combinations of parameters and suggest more reasonable ranges for these parameters. icsearcher applies a metaheuristic search algorithm to find configurations with a high probability of driving the drone into unstable states. In particular, icsearcher adopts a machine learning-based predictor to assist the searcher in evaluating the fitness of configuration. Finally, leveraging searched incorrect configurations, icsearcher can summarize the feasible ranges through multi-objective optimization. icsearcher applies a predictor to guide the search, which eliminates the need for realistic/simulation executions when evaluating configurations and further promotes search efficiency. We have carried out experimental evaluations of icsearcher in different control programs. The evaluation results show that the system successfully reports potentially incorrect configurations, of which over 94%94% leads to unstable states.
format text
author HAN, Ruidong
MA, Siqi
LI, Juanru
NEPAL, Surya
LO, David
MA, Zhuo
MA, Jianfeng
author_facet HAN, Ruidong
MA, Siqi
LI, Juanru
NEPAL, Surya
LO, David
MA, Zhuo
MA, Jianfeng
author_sort HAN, Ruidong
title Range specification bug detection in flight control systems through fuzzing
title_short Range specification bug detection in flight control systems through fuzzing
title_full Range specification bug detection in flight control systems through fuzzing
title_fullStr Range specification bug detection in flight control systems through fuzzing
title_full_unstemmed Range specification bug detection in flight control systems through fuzzing
title_sort range specification bug detection in flight control systems through fuzzing
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9263
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