Research on side channel attack based on bagging ensemble strategy

Jing Wang; Bo Gao; Yingjian Yan

doi:10.1117/12.2647073

7 September 2022 Research on side channel attack based on bagging ensemble strategy

Jing Wang, Bo Gao, Yingjian Yan

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123292K (2022) https://doi.org/10.1117/12.2647073
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Deep learning technology is widely used in side-channel attacks, improving the ability of profiling side-channel attacks. A model based on Bagging ensemble strategy is proposed to improve the classification performance of neural networks in Deep Learning based Side Channel Attacks based. In this model, the weighted voting method based on class probability is used to combine each base model, and the difference of each classifier is fully combined. The weighted coefficient is calculated by reciprocal method to improve the classification accuracy. Finally, the maximum likelihood estimation is used to recover the key to further improve the attack efficiency. Experimental results show that when attacking open data sets, the side channel attack method based on Bagging integrated strategy improved model has a higher success rate, and the number of attacks required for successful attack can be reduced by more than 16.3%.

Citation Download Citation

Jing Wang, Bo Gao, and Yingjian Yan "Research on side channel attack based on bagging ensemble strategy", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123292K (7 September 2022); https://doi.org/10.1117/12.2647073

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