Leakage Model-flexible Deep Learning-based Side-channel Analysis
Authors
Lichao Wu, Azade Rezaeezade, Amir Ali-pour, Guilherme Perin, Stjepan Picek
Lichao Wu
Technical University of Darmstadt, Darmstadt, Germany
Azade Rezaeezade
Delft University of Technology, Delft, The Netherlands
Amir Ali-pour
École de technologie supérieure, Montreal, Canada
Guilherme Perin
Leiden University, Leiden, The Netherlands
Stjepan Picek
Radboud University, Nijmegen, The Netherlands
stjepan dot picek at ru dot nl
stjepan dot picek at ru dot nl
Abstract
Profiling side-channel analysis has gained widespread acceptance in both academic and industrial realms due to its robust capacity to unveil protected secrets, even in the presence of countermeasures. To harness this capability, an adversary must access a clone of the target device to acquire profiling measurements, labeling them with leakage models. The challenge of finding an effective leakage model, especially for a protected dataset with a low signal-to-noise ratio or weak correlation between actual leakages and labels, often necessitates an intuitive engineering approach, as otherwise, the attack will not perform well.
In this paper, we introduce a deep learning approach with a flexible leakage model, referred to as the multi-bit model. Instead of trying to learn a pre-determined representation of the target intermediate data, we utilize the concept of the stochastic model to decompose the label into bits. Then, the deep learning model is used to classify each bit independently. This versatile multi-bit model can adjust to existing leakage models like the Hamming weight and Most Significant Bit while also possessing the flexibility to adapt to complex leakage scenarios. To further improve the attack efficiency, we extend the multi-bit model to profile all 16 subkey bytes simultaneously, which requires negligible computational effort. The experimental results show that the proposed methods can efficiently break all key bytes across four considered datasets while the conventional leakage models fail. Our work signifies a significant step forward in deep learning-based side-channel attacks, showcasing a high degree of flexibility and efficiency with the proposed leakage model.
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History
Submitted: 2024-07-09
Accepted: 2024-09-02
Published: 2024-10-07
Accepted: 2024-09-02
Published: 2024-10-07
How to cite
Lichao Wu, Azade Rezaeezade, Amir Ali-pour, Guilherme Perin, and Stjepan Picek, Leakage Model-flexible Deep Learning-based Side-channel Analysis. IACR Communications in Cryptology, vol. 1, no. 3, Oct 07, 2024, doi: 10.62056/ay4c3txol7.
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