Paper 2024/1437
UNIDLE: A Unified Framework for Deep Learning-based Side-channel Analysis
Abstract
Side-channel analysis (SCA) exploits the dependency of a cryptographic device's power consumption or electromagnetic (EM) emissions on the data being processed to extract secret keys. While deep learning (DL) has advanced SCA, most existing models struggle to scale to long traces and tend to be effective only against specific countermeasures. Therefore, their applicability is limited in implementations that employ multiple countermeasures simultaneously. This paper introduces UNIDLE, a UNIfied framework for Deep LEarning-based SCA, for building scalable and robust models for long traces. UNIDLE employs a hierarchical divide-and-conquer strategy: it segments each trace into smaller parts, which are independently processed by base models, and then aggregates their outputs using a top-level model. This approach enables efficient information extraction from long traces while making the model robust against combinations of masking and desynchronization-based countermeasures. UNIDLE also naturally supports multi-task learning, a useful DL approach for attacking countermeasures such as shuffling, while being less susceptible to the capacity bottlenecks observed in existing multi-task models. Leveraging this framework, we develop HierNet, a novel DL model that integrates a shift-invariant base model and a transformer-based top-level model. HierNet demonstrates strong empirical performance across three datasets of masked implementations, reaching the guessing entropy 1 with fewer than 50 attack traces--where several state-of-the-art benchmark models fail even with up to 5K attack traces. Experiments on traces protected by shuffling show that HierNet can reach the guessing entropy 1 using 95% fewer attack traces than a state-of-the-art multi-task benchmark while requiring only one-tenth of the model size.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- Side-Channel AnalysisDeep LearningTransformer NetworkShift-Invariance
- Contact author(s)
-
suvadeep hajra @ gmail com
debdeep mukhopadhyay @ gmail com
soumichatterjee0409 @ gmail com - History
- 2025-07-07: last of 3 revisions
- 2024-09-14: received
- See all versions
- Short URL
- https://ia.cr/2024/1437
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2024/1437, author = {Suvadeep Hajra and Debdeep Mukhopadhyay and Soumi Chatterjee}, title = {{UNIDLE}: A Unified Framework for Deep Learning-based Side-channel Analysis}, howpublished = {Cryptology {ePrint} Archive, Paper 2024/1437}, year = {2024}, url = {https://eprint.iacr.org/2024/1437} }