Combined Distinguishers to Enhance the Accuracy and Success of Side Channel Analysis
M. Ali Vosoughi, Selçuk Köse · 2019
For the first time, the combination of mutual information analysis and correlation power analysis is proposed to enhance the accuracy and success rate of side channel analysis. Using the k-nearest-neighborhood (KNN) algorithm, correlation power analysis is combined with mutual information analysis to classify various possible keys to two classes of correct and wrong keys. The advantage of the combination of the distinguishers is two fold. First, the accuracy of the estimation is enhanced due to availability of multiple possible values for the correct key. Second, the number of measurements required to disclose the correct key is reduced by combining the distinguishers. The effectiveness of combined distinguisher is verified by extensive simulations. The number of measurements required to perform a side channel attack with a success rate of 90% is improved, respectively, by 20% and 49%, as compared to individual correlation power analysis and mutual information analysis.