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A novel points of interest selection method for SVM-based profiled attacks

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A novel points of interest selection method for SVM-based profiled attacks

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Currently, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine (SVM), are currently used to improve the effectiveness of the attack. One issue of using SVM-based profiled attack is extracting points of interest (POIs), or features from power traces. Our work proposes a novel method for POIs selection of power traces based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). VMD is used to decompose the power traces into sub-signals (modes) and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack.

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