Paper 2025/1954

Neural Leakage Model: Correlation Power Analysis with Profiled Leakage Model using Deep Neural Networks

Abstract

Side-channel analysis (SCA) exploits physical leakages such as power consumption or electromagnetic emissions to extract secret information from cryptographic implementations. Leakage model is the critical link between observable physical emanations (e.g., power consumption) and internal cryptographic states. When a leakage model fails to match the device’s underlying physical leakage, even powerful attacks like Correlation Power Analysis (CPA) are rendered ineffective. This fundamental challenge limits the success of traditional SCA, especially against noisy or masked targets. In this work, we introduce the Neural Leakage Model (NLM), a novel neural network architecture trained to learn and accurately characterize complex physical leakage to enhance CPA. Moving beyond NLM's superior attack performance, we directly address the critical ``black box'' problem in deep learning-based side-channel analysis (DLSCA) by proposing a novel methodology to transform the trained NLM into a mathematically equivalent, closed-form polynomial expression. This provides interpretability, offering evaluators transparent insight into the precise leakage model the network has learned. We validate NLM’s performance across four diverse datasets spanning three platforms, from low-end microcontrollers to high-end multi-core ARM systems and dedicated FPGAs. Notably, NLM successfully recovers the secret key from the highly challenging and noisy CHES2025 dataset, representing the first known successful CPA attack on this benchmark. Furthermore, NLM demonstrates superior or comparable performance when extended to higher-order CPA against masking countermeasures. Our findings establish NLM as a powerful, generalizable, and interpretable approach to modern side-channel analysis.