TY - GEN
T1 - START
T2 - 33rd IEEE International Symposium on Software Reliability Engineering, ISSRE 2022
AU - Leach, Kevin
AU - Timperley, Christopher S.
AU - Angstadt, Kevin
AU - Nguyen-Tuong, Anh
AU - Hiser, Jason
AU - Paulos, Aaron
AU - Pal, Partha
AU - Hurley, Patrick
AU - Thomas, Carl
AU - Davidson, Jack W.
AU - Forrest, Stephanie
AU - Goues, Claire Le
AU - Weimer, Westley
N1 - Funding Information:
This research was funded by AFRL (#FA8750-15-2-0075); the authors are grateful for their support. Any opinions, findings, or recommendations expressed are those of the authors and do not necessarily reflect those of the US Government.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - From delivering groceries and vital medical supplies to driving trucks and passenger vehicles, society is becoming increasingly reliant on autonomous vehicles (AVs), It is therefore vital that these systems be resilient to adversarial actions, perform mission-critical functions despite known and unknown vulnerabilities, and protect and repair themselves during or after operational failures and cyber-attacks. While techniques have been proposed to address individual aspects of software resilience, vulnerability assessment, automated repair, and invariant detection, there is no approach that provides end-to-end trusted and resilient mission operation and repair on AVs. In this paper, we describe our experience of building START,1 a framework that provides increased resilience, accurate vul-nerability assessment, and trustworthy post-repair operation in autonomous vehicles. We combine techniques from binary analysis and rewriting, runtime monitoring and verification, auto-mated program repair, and invariant detection that cooperatively detect and eliminate a swath of software security vulnerabilities in cyberphysical systems. We evaluate our framework using an autonomous vehicle simulation platform, demonstrating its holistic applicability to AVs.
AB - From delivering groceries and vital medical supplies to driving trucks and passenger vehicles, society is becoming increasingly reliant on autonomous vehicles (AVs), It is therefore vital that these systems be resilient to adversarial actions, perform mission-critical functions despite known and unknown vulnerabilities, and protect and repair themselves during or after operational failures and cyber-attacks. While techniques have been proposed to address individual aspects of software resilience, vulnerability assessment, automated repair, and invariant detection, there is no approach that provides end-to-end trusted and resilient mission operation and repair on AVs. In this paper, we describe our experience of building START,1 a framework that provides increased resilience, accurate vul-nerability assessment, and trustworthy post-repair operation in autonomous vehicles. We combine techniques from binary analysis and rewriting, runtime monitoring and verification, auto-mated program repair, and invariant detection that cooperatively detect and eliminate a swath of software security vulnerabilities in cyberphysical systems. We evaluate our framework using an autonomous vehicle simulation platform, demonstrating its holistic applicability to AVs.
KW - au-tomated program repair
KW - autonomous vehicles
KW - availability
KW - resilience
UR - http://www.scopus.com/inward/record.url?scp=85145877047&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145877047&partnerID=8YFLogxK
U2 - 10.1109/ISSRE55969.2022.00018
DO - 10.1109/ISSRE55969.2022.00018
M3 - Conference contribution
AN - SCOPUS:85145877047
T3 - Proceedings - International Symposium on Software Reliability Engineering, ISSRE
SP - 73
EP - 84
BT - Proceedings - 2022 IEEE 33rd International Symposium on Software Reliability Engineering, ISSRE 2022
PB - IEEE Computer Society
Y2 - 31 October 2021 through 3 November 2021
ER -