Leveraging Ensemble Learning for Side Channel Analysis on Masked AES
Feng Gao, Baolei Mao, Lingjuan Wu, Zongmin Wang, Dejun Mu, Wei Hu · 2021 7th International Conference on Computer and Communications (ICCC) · 2021
Symmetric cryptographic functions are known to leak secret information through power side channels. Although masking provides an effective mitigation to such leakage, these protection designs are vulnerable to advanced side-channel analysis based on machine learning. In this work, we propose a framework to leverage ensemble learning for power analysis on a masked AES implementation. Before the key recovery, we use machine learning techniques including decision tree, random forest, BP nerual network, and ensemble learning to recover the masks. We also implement data set enhancement through oversampling techniques including random oversampling, synthetic minority over-sampling technique (SMOTE), k-means-SMOTE, Borderline-SMOTE, and SMOTENC. In addition, we explore different ensemble learning methods on key recovery of power side-channel attacks. Experimental results on DPA contest v4 power traces show that ensemble learning methods improve the attack success rate by approximately 11.29% on average over neural network attacks. The results also reveal that data set enhancement can help reduce the variations in attack success rates between different key bytes.