End-to-End Non-profiled Side-Channel Analysis on Long Raw Traces

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 min to recover the key from the leakage interval with 1,400 feature points, our framework only requires 6,000 traces in 13 min 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.

External IDs:dblp:conf/esorics/YuWQZSCLZGH25