Welcome to the resource topic for 2025/1427
Title:
End-to-End Non-Profiled Side-Channel Analysis on Long Raw Traces
Authors: Jintong Yu, Yuxuan Wang, Shipei Qu, Yubo Zhao, Yipeng Shi, Pei Cao, Xiangjun Lu, Chi Zhang, Dawu Gu, Cheng Hong
Abstract:With the advancement of deep learning techniques, Deep
Learning-based Non-profiled Side-Channel Analysis (DL-NSCA) can automatically learn and combine features, making it a promising method
that can skip the manual and precise selection of Points of Interest (PoIs).
Existing DL-NSCA methods assume that the attacker can identify a
short leakage interval (usually less than 5000 points) containing PoIs
from raw traces (more than 100,000 points) and then feed the leakage
interval into the neural network to recover the key. However, in practice,
the attacker often faces a black-box scenario with unknown underlying implementations, making locating the short interval from raw traces
challenging, especially when masking countermeasures exist. To address
this issue, we propose a lightweight end-to-end DL-NSCA model called
convWIN-MCR, which consists of a performance-optimizing component,
convWIN, and an accelerator component, MCR. It can efficiently process
raw traces without the need to manually identify the short leakage interval. On the public dataset ASCADv1, while the state-of-the-art model
Multi-Output Regression (MOR) requires 28,000 traces and 24 minutes
to recover the key from the leakage interval with 1,400 feature points, our
framework only requires 6,000 traces in 13 minutes to directly analyze
raw traces with 250,000 feature points. To further validate the practical
applicability of our framework, we successfully crack a commercial USIM
card by analyzing its raw traces and recovering its 128-bit AES key.
ePrint: https://eprint.iacr.org/2025/1427
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