| Title: | PN-SCA : a high generalization and fast profiled SCA based on prototypical networks |
|---|
| Authors: | ID Ou, Yu (Author)
ID Wei, Yongzhuang (Author)
ID Ou, Changhai (Author)
ID Pašalić, Enes (Author) |
|---|
| Files: |  RAZ_Ou_Yu_2026.pdf (3,78 MB)
MD5: CFC7C8B2CE35B2FA90F448098D651F9F
 https://link.springer.com/article/10.1007/s10836-026-06219-4
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | IAM - Andrej Marušič Institute
|
|---|
| Abstract: | Recently, there has been a growing interest in scenarios that employ a few (imbalanced) power traces for deep learning based side-channel analysis (DL-SCA). Finding a lightweight DL-SCA architecture that is more generalizable and faster to learn is of great importance when handling such situations. In this work, an initial analysis is conducted on the capability of learning and the susceptibility to attacks of prevalent DL-SCA models, focusing on cases that culminate in an unsuccessful attack. Subsequently, a meta-learning technique, known as prototypical networks, is delineated for the construction of a lightweight deep learning framework. In contrast to conventional DL-SCA models, the proposed architecture, designated as PN-SCA, does not predict the probability of a power trace belonging to an intermediate value in classification. Instead, it facilitates the learning of an encoder capable of embedding power consumption data within a latent space, while also establishing templates, or prototypes, for diverse categories. Moreover, we developed a metric that is specifically intended for the selection of hyperparameters due to the unique training phase of PN-SCA. Finally, four distinct scenarios are established with few (including one imbalanced) power traces to evaluate the correctness of our architecture. The results clearly illustrate the advantages of our PN-SCA in terms of generalization, reduced training costs (with a decrease in profiling traces by over 50%), and significantly enhanced attack effect (with a reduction of power traces requirements by over 90%), thus demonstrating notable improvements over existing methodologies |
|---|
| Keywords: | side-channel analysis, deep learning based SCA, prototypical networks, few-shot learning |
|---|
| Publication version: | Author Accepted Manuscript |
|---|
| Publication date: | 12.02.2026 |
|---|
| Year of publishing: | 2026 |
|---|
| Number of pages: | str. 77-94 |
|---|
| Numbering: | Vol. 42, iss. 1 |
|---|
| PID: | 20.500.12556/RUP-23063  |
|---|
| UDC: | 004.8 |
|---|
| ISSN on article: | 1573-0727 |
|---|
| DOI: | 10.1007/s10836-026-06219-4 |
|---|
| COBISS.SI-ID: | 278763779 |
|---|
| Publication date in RUP: | 20.05.2026 |
|---|
| Views: | 36 |
|---|
| Downloads: | 2 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
