TY - GEN
T1 - Gray-box adversarial testing for control systems with machine learning components
AU - Yaghoubi, Shakiba
AU - Fainekos, Georgios
N1 - Funding Information:
This research was partially funded by the NSF awards CNS 1350420 and IIP 1361926, and the NSF I/UCRC Center for Embedded Systems.
Publisher Copyright:
© 2019 ACM.
PY - 2019/4/16
Y1 - 2019/4/16
N2 - Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics. However, despite the extensive research, NN-based controllers have not been adopted by the industry for safety critical systems. The primary reason is that systems with learning based controllers are notoriously hard to test and verify. Even harder is the analysis of such systems against system-level specifications. In this paper, we provide a gradient based method for searching the input space of a closed-loop control system in order to find adversarial samples against some system-level requirements. Our experimental results show that combined with randomized search, our method outperforms Simulated Annealing optimization.
AB - Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics. However, despite the extensive research, NN-based controllers have not been adopted by the industry for safety critical systems. The primary reason is that systems with learning based controllers are notoriously hard to test and verify. Even harder is the analysis of such systems against system-level specifications. In this paper, we provide a gradient based method for searching the input space of a closed-loop control system in order to find adversarial samples against some system-level requirements. Our experimental results show that combined with randomized search, our method outperforms Simulated Annealing optimization.
KW - Neural network
KW - Optimization
KW - Testing and verification
UR - http://www.scopus.com/inward/record.url?scp=85064967114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064967114&partnerID=8YFLogxK
U2 - 10.1145/3302504.3311814
DO - 10.1145/3302504.3311814
M3 - Conference contribution
AN - SCOPUS:85064967114
T3 - HSCC 2019 - Proceedings of the 2019 22nd ACM International Conference on Hybrid Systems: Computation and Control
SP - 179
EP - 184
BT - HSCC 2019 - Proceedings of the 2019 22nd ACM International Conference on Hybrid Systems
PB - Association for Computing Machinery, Inc
T2 - 22nd ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2019
Y2 - 16 April 2019 through 18 April 2019
ER -