TY - GEN
T1 - Moving Target Defense Strategy in Critical Embedded Systems
T2 - 26th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2021
AU - Ayrault, Maxime
AU - Borde, Etienne
AU - Kuhne, Ulrich
AU - Leneutre, Jean
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Moving Target Defense (MTD) is a promising de-fense technique that aims to break the asymmetry between attacker and defender by reconfiguring a system's assets. When used in a critical embedded system, non-functional constraints limit the set of usable MTD techniques, therefore limiting the entropy of reconfigurations. It is therefore necessary to compute an optimal moving target strategy in order to reduce the time between reconfigurations, while managing their impact on the quality of service provided to users. In this paper, we propose a game-theoretic approach to define an optimal moving target defense strategy. Our approach translates risk analysis parame-ters into a Bayesian Stackelberg game, which we translate to a mixed integer linear program. We validate our approach on an industrial case study from the automotive domain, showing the practical usability of our approach. In further experiments, we assess the scalability of our method, as well as the solution's stability in case new vulnerabilities are discovered after the deployment of the system.
AB - Moving Target Defense (MTD) is a promising de-fense technique that aims to break the asymmetry between attacker and defender by reconfiguring a system's assets. When used in a critical embedded system, non-functional constraints limit the set of usable MTD techniques, therefore limiting the entropy of reconfigurations. It is therefore necessary to compute an optimal moving target strategy in order to reduce the time between reconfigurations, while managing their impact on the quality of service provided to users. In this paper, we propose a game-theoretic approach to define an optimal moving target defense strategy. Our approach translates risk analysis parame-ters into a Bayesian Stackelberg game, which we translate to a mixed integer linear program. We validate our approach on an industrial case study from the automotive domain, showing the practical usability of our approach. In further experiments, we assess the scalability of our method, as well as the solution's stability in case new vulnerabilities are discovered after the deployment of the system.
KW - Embedded system
KW - Game Theory
KW - MILP
KW - MTD
UR - https://www.scopus.com/pages/publications/85125007622
U2 - 10.1109/PRDC53464.2021.00014
DO - 10.1109/PRDC53464.2021.00014
M3 - Conference contribution
AN - SCOPUS:85125007622
T3 - Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC
SP - 27
EP - 36
BT - Proceedings - 2021 IEEE 26th Pacific Rim International Symposium on Dependable Computing, PRDC 2021
PB - IEEE Computer Society
Y2 - 1 December 2021 through 4 December 2021
ER -