Paper 2025/541

Physical Design-Aware Power Side-Channel Leakage Assessment Framework using Deep Learning

Dipayan Saha, University of Florida
Jingbo Zhou, University of Florida
Farimah Farahmandi, University of Florida
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
Creative Commons Attribution
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}
}
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