Getting More from PCA: First Results of Using Principal Component Analysis for Extensive Power Analysis

Differential Power Analysis (DPA) is commonly used to obtain information about the secret key used in cryptographic devices. Countermeasures against DPA can cause power traces to be misaligned, which reduces the effectiveness of DPA. Principal Component Analysis (PCA) is a powerful tool, which is used in different research areas to identify trends in a data set. Principal Components are introduced to describe the relationships within the data. The largest principal components capture the data with the largest variance. These Principal Components can be used to reduce the noise in a data set or to transform the data set in terms of these components. We propose the use of Principal Component Analysis to improve the correlation for the correct key guess for DPA attacks on software DES traces and show that it can also be applied for other algorithms. We also introduce a new way of determining key candidates by calculating the absolute average value of the correlation traces after a DPA attack on a PCA-transformed trace. We conclude that Principal Component Analysis can successfully be used as a preprocessing technique to reduce the noise in a trace set and improve the correlation for the correct key guess using Differential Power Analysis attacks.