Non-Profiled Side-Channel Attack Based on Deep Learning Using Picture Trace

Over the years, deep learning algorithms have advanced a lot and any innovation in are demonstrated benchmarked for image classification. Several other field including side-channel analysis (SCA) recently adopted with great success. In SCA, typically working 1-dimensional (1-D) data. this work, we propose unique method to improve based by converting measurements from raw-trace of 1-dimension data on float or byte into picture-formatted trace that has information position. We demonstrate why “Picturization” is more suitable compare how input hidden layers interact each raw picture form. As one potential application, use Binarized Neural Network (BNN) relies BNN's natural properties variables. addition, novel criterion attack success failure statistical confidence level rather than determination correct key using ranking system.