基于集成学习的功耗分析研究

Research of Power Analysis Based on Ensemble Model

  • 摘要: 针对单模型分类算法在训练样本数量较少时成功率偏低的问题,提出一种集成学习算法,并在DPA_Contest_V4数据集上进行实验。首先使用传统方法破解循环掩码,再使用SVM(support vector machine)、随机森林和k近邻(k-nearest neighbor,kNN)等分类算法进行训练和预测,最后将这些模型的结果集成。实验结果表明,集成模型优于单一模型,尤其当训练集中的能量迹数目较少时集成模型的成功率比单一模型高10%左右。

     

    Abstract: Aiming at the problem that the single model classification algorithm has a low success rate when the number of training samples is low, an ensemble learning algorithm is presented in this paper. The experiment was conducted by applying DPA_Contest_V4 dataset. First the traditional method is used to break the mask, and then SVM, RF and kNN classification algorithms are applied to train and predict. Finally, the results of these models are combined as an ensemble model. The experimental results show that the integrated model is superior to the single model, and the success rate of the ensemble model can be about 10% higher than that of the single model especially when the number of training samples is low.

     

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