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Model design and parameter optimization of CNN for side-channel cryptanalysis.

Yun Lin Liu¹, Yan Kai Chen¹, Wei Xiong Li¹

¹Center of Equipment Simulation Training, Shijiazhuang Campus of the Army Engineering University, Shijiazhuang, Hebei, China.

Peerj. Computer Science

|February 3, 2022

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This study introduces CNNSCAnew, an improved convolutional neural network side-channel attack model. CNNSCAnew enhances cryptanalysis performance and efficiency, outperforming existing models in key recovery attacks.

Area of Science:

Cryptography
Computer Science
Machine Learning

Background:

Convolutional Neural Network Side-Channel Analysis (CNNSCA) is effective for cryptographic attacks.
Existing VGG-CNNSCA and Alex-CNNSCA models have suboptimal learning ability and performance.
Current models suffer from low accuracy, long training times, and high resource consumption.

Purpose of the Study:

To improve the overall performance of CNNSCA models.
To enhance accuracy, reduce training time, and optimize resource utilization.
To develop an optimized CNNSCA model design and hyperparameter tuning strategy.

Main Methods:

Studied CNN architecture for side-channel analysis (SCA) and derived the CNN core algorithm for 1D data leakage.
Designed a new CNNSCA basic model integrating VGG and Alexnet advantages, embedding a Squeeze-and-Excitation (SE) module.

Keywords:

Alexnet CNN Hyperparameter SEnet Side-channel analysis VGG

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Applied the model to the ASCAD dataset, optimized hyperparameters, and established a benchmark for parameter determination.

Main Results:

Developed CNNSCAnew, an optimized architecture for attacking unprotected encryption devices.
Achieved converged guessing entropy evaluation results of 61.
Reduced total key recovery time to approximately 30 minutes, surpassing other CNNSCA models.

Conclusions:

The novel CNNSCAnew model significantly improves cryptanalysis performance and efficiency.
Hyperparameter optimization and SE module integration are key to enhanced CNNSCA models.
The developed approach offers a more effective and efficient method for side-channel attacks.