Paper 2025/541
Physical Design-Aware Power Side-Channel Leakage Assessment Framework using Deep Learning
Abstract
Power side-channel (PSC) vulnerabilities present formidable challenges to the security of ubiquitous microelectronic devices in mission-critical infrastructure. Existing side-channel assessment techniques mostly focus on post-silicon stages by analyzing power profiles of fabricated devices, suffering from low flexibility and prohibitively high cost while deploying security countermeasures. While pre-silicon PSC assessments offer flexibility and low cost, the true nature of the power signatures cannot be fully captured through RTL or gate-level design. Although physical design-level analysis provides precise power traces, collecting data is time and resource-consuming at the layout level. To address this challenge, we propose, for the first time, a fast and efficient physical design-level PSC assessment framework using a graph neural network (GNN). This framework predicts dynamic power traces for new layouts, using them to assess physical design security through metrics evaluation. Our experiments on AES-GF layout implementations achieve a tremendous 133 times speedup compared to conventional simulation-based flow without sacrificing substantial accuracy.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Published elsewhere. IEEE International Symposium on Circuits and Systems (IEEE ISCAS 2025)
- Keywords
- Side-Channel AnalysisPhysical DesignEDADeep LearningGraph Neural Network
- Contact author(s)
-
dsaha @ ufl edu
jingbozhou @ ufl edu
farimah @ ece ufl edu - History
- 2025-03-25: approved
- 2025-03-24: received
- See all versions
- Short URL
- https://ia.cr/2025/541
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2025/541, author = {Dipayan Saha and Jingbo Zhou and Farimah Farahmandi}, title = {Physical Design-Aware Power Side-Channel Leakage Assessment Framework using Deep Learning}, howpublished = {Cryptology {ePrint} Archive, Paper 2025/541}, year = {2025}, url = {https://eprint.iacr.org/2025/541} }