Power Analysis Attack Based on BS-XGboost Scheme

The power attack is a type of side-channel attack that involves measuring the power consumption of a device to extract secret information. By analyzing power consumption variations, an attacker can deduce the secret key used in the operation. In a class-imbalanced dataset, where the number of samples in one class is much smaller than the other, the power consumption patterns during cryptographic operations may be different for each class. The BorderLine-SMOTE data enhancement scheme was used to generate synthetic samples near the boundaries or at a greater distance from the existing samples, and through these modifications it helps to increase the diversity of the synthetic samples and reduce the risk of overfitting. XGBoost is then used as a classifier to classify the power curves. To evaluate the efficacy of the proposed method, it was applied to the DPA V4 dataset. The results indicated that the original data, when augmented using the Borderline-SMOTE + XGBoost approach, exhibited a substantial improvement in classification precision of up to 34%, outperforming DUAN’s method.