  Various studies among side-channel attacks have tried to extract information
through leakages from electronic devices to reach the instruction flow of some
appliances. However, previous methods highly depend on the resolution of traced
data. Obtaining low-noise traces is not always feasible in real attack
scenarios. This study proposes two deep models to extract low and high-level
features from side-channel traces and classify them to related instructions. We
aim to evaluate the accuracy of a side-channel attack on low-resolution data
with a more robust feature extractor thanks to neural networks. As
inves-tigated, instruction flow in real programs is predictable and follows
specific distributions. This leads to proposing a LSTM model to estimate these
distributions, which could expedite the reverse engineering process and also
raise the accuracy. The proposed model for leakage classification reaches
54.58% accuracy on average and outperforms other existing methods on our
datasets. Also, LSTM model reaches 94.39% accuracy for instruction prediction
on standard implementation of cryptographic algorithms.
