Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
4-2025
Abstract
The analysis of timed properties of programs is a complex task, as it is highly dependent on both the software and the hardware. In this work, we propose a framework for modeling with timed formal models the execution of programs, taking into account the micro-architecture of the machine on which it executes. We model both the program, at the instruction set architecture level, and the hardware, including the processor micro-architecture, using time Petri nets. Our implementation uses the ARM Cortex-M instruction set architecture and a hardware architecture representative of microcontrollers used in IoT nodes. The whole translation is fully automated and allows, starting from binary code, to automatically produce the models usable by the state-of-the-art time Petri net model checker Roméo. In addition, and as a proof of concept, we show how we can check, and enforce to some extent, opacity properties on programs, leveraging the ability of Roméo to verify a parametric timed extension of the classical computation tree logic.
Keywords
execution time, formal methods, program verification, timing attacks
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
SAC '25: Proceedings of the 40th Annual ACM Symposium on Applied Computing, March 30 - April 4, Catania
First Page
1998
Last Page
2006
ISBN
9798400706295
Identifier
10.1145/3672608.3707861
Publisher
Association for Computing Machinery
City or Country
New York
Citation
André, Étienne; Béchennec, Jean-Luc; Chattopadhyay, Sudipta; Faucou, Sebastien; Lime, Didier; Marinho, Dylan; Roux, Olivier H.; and Jun SUN.
Verifying timed properties of programs in IoT nodes using parametric time petri nets. (2025). SAC '25: Proceedings of the 40th Annual ACM Symposium on Applied Computing, March 30 - April 4, Catania. 1998-2006.
Available at: https://ink.library.smu.edu.sg/sis_research/10234
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/10.1145/3672608.3707861