Comparison of Profiling Power Analysis Attacks Using Templates and Multi-Layer Perceptron Network

In recent years, the cryptographic community has explored new approaches of power analysis based on machine learning models such as Support Vector Machine (SVM), MultiLayer Perceptron (MLP) or Random Forest (RF). Realized experiments proved that the method based on MLP can provide almost 100% success rate after optimization. Nevertheless, this description of results is based on the first order success rate that is not enough satisfactory because this value can be deceiving. Moreover, the power analysis method based on MLP has not been compared with other well-known approaches such as template attacks or stochastic attacks yet. In this paper, we introduce the first fair comparison of power analysis attacks based on MLP and templates. The comparison is accomplished by using the identical data set and number of interesting points in power traces. We follow the unified framework for implemented sidechannel attacks therefore we use guessing entropy as a metric of comparison. Keywords—Power Analysis, Neural Network, Template Attack, Comparison.