Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
1-2020
Abstract
In this work, we study the problem of verification of systems in the presence of attackers using bounded model checking. Given a system and a set of security requirements, we present a methodology to generate and classify attackers, mapping them to the set of requirements that they can break. A naive approach suffers from the same shortcomings of any large model checking problem, i.e., memory shortage and exponential time. To cope with these shortcomings, we describe two sound heuristics based on cone-of-influence reduction and on learning, which we demonstrate empirically by applying our methodology to a set of hardware benchmark systems.
Discipline
Information Security
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 21st International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2020, New Orleans, LA, January 19-21
First Page
1
Last Page
23
Identifier
10.1007/978-3-030-39322-9_11
City or Country
New Orleans, LA
Citation
ROTHSTEIN-MORRIS, Eric; SUN, Jun; and CHATTOPADYAY, Sudipta.
Systematic classification of attackers via bounded model checking. (2020). Proceedings of the 21st International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2020, New Orleans, LA, January 19-21. 1-23.
Available at: https://ink.library.smu.edu.sg/sis_research/4634
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-030-39322-9_11