Peek into the Black-Box: Interpretable Neural Network using SAT Equations in Side-Channel Analysis

Abstract

Deep neural networks (DNN) have become a significant threat to the security of cryptographic implementations with regards to side-channel analysis (SCA), as they automatically combine the leakages without any preprocessing needed, leading to a more efficient attack. However, these DNNs for SCA remain mostly black-box algorithms that are very difficult to interpret. Benamira et al. recently proposed an interpretable neural network called Truth Table Deep Convolutional Neural Network (TT-DCNN), which is both expressive and easier to interpret. In particular, a TT-DCNN has a transparent inner structure that can entirely be transformed into SAT equations after training. In this work, we analyze the SAT equations extracted from a TT-DCNN when applied in SCA context, eventually obtaining the rules and decisions that the neural networks learned when retrieving the secret key from the cryptographic primitive (i.e., exact formula). As a result, we can pinpoint the critical rules that the neural network uses to locate the exact Points of Interest (PoIs). We validate our approach first on simulated traces for higher-order masking. However, applying TT-DCNN on real traces is not straightforward. We propose a method to adapt TT-DCNN for application on real SCA traces containing thousands of sample points. Experimental validation is performed on software-based ASCADv1 and hardware-based AES_HD_ext datasets. In addition, TT-DCNN is shown to be able to learn the exact countermeasure in a best-case setting.

Keywords

• Side-channel
• Neural Network
• Deep Learning
• Profiling attack
• Interpretability
• SAT