On features suitable for power analysis - filtering the contributing features for symmetric key recovery

Side-channel attacks have left the traditional methods of cryptanalysis far behind. The algorithms are mathematically secure, but the side-channel leakage poses a serious security threat. Innovative machine-learning classification methods have remarkably reduced the sampling time as well as the time required to recover the key. However, these results are constrained by high dimensionality, i.e. complex feature data increases the classification time, and at times results in false classification. In this paper, we a im to narrow down the feature space and determine which features contribute most, towards better classification accuracy, for key retrieval from an AES implementation running over Kintex-7. We have provided a comparison of classifying the key bit as 0 or 1 with a varying number of samples and different sets of features. This paper gives practical results of different properties becoming features for extracted power signals using both feature selection and extraction methods.