In the power analysis attack, when the Hamming weight model is used to describe the power consumption of the chip operation data, the result of the random forest (RF) algorithm is not ideal, so a random forest classification method based on synthetic minority oversampling technique (SMOTE) is proposed. It compensates for the problem that the random forest algorithm is affected by the data imbalance and the classification accuracy of the minority classification is low, which improves the overall classification accuracy rate. The experimental results show that when the training set data is 800, the random forest algorithm predicts the correct rate of 84%, but the classification accuracy of the minority data is 0%, and the SMOTE-based random forest algorithm improves the prediction accuracy of the same set of test data by 91%. The classification accuracy rate of a few categories has increased from 0% to 100%.

中文翻译：

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不平衡数据功耗分析攻击的研究与实现

在功耗分析攻击中，当使用汉明权重模型描述芯片操作数据的功耗时，随机森林（RF）算法的结果并不理想，因此基于合成少数群体过采样技术的随机森林分类方法（SMOTE）。弥补了随机森林算法受数据不平衡影响，少数分类的分类精度低的问题，提高了整体分类的准确率。实验结果表明，当训练集数据为800时，随机森林算法预测正确率为84％，而少数数据的分类精度为0％，基于SMOTE的随机森林算法提高了预测精度。同一组测试数据减少了91％。