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