MLP and CNN-based Classification for Points of Interest in Side-Channel Attacks
Hanwen Feng, Weiguo Lin, Wenqian Shang · 2019
There are lots of different sample points in a single trace, the each sample point containing some leakage information is useful to obtain the key when a chip encrypt plaintext with the key, these points of interest in a trace could be extracted and a new trace is formed sequentially. If using this shorter trace could improve the performance of classification in neural networks during side-channel attacks and to reduce the amount of traces required in the classification, and it means that these sample points are indeed useful and contain lots of information needed for side-channel attacks. In this paper, different amount of points of interest extracted from traces in ASCAD to form a new kind of traces as input data feed into neural networks including Multi-Layer Perceptron, Convolutional Neural Networks. In order to compare with the result of POI-traces, Principal Components Analysis was also used to shorter the length of the original trace in ASCAD, so that its length is the same as POI-traces'. About the results, the classification results with ASCAD traces are the worst using MLP or CNN, the results of PCA-traces which contain only 100 sample points using MLP are the best, and the results of POI-traces containing 300 sample points are the best result using CNN. So when using neural networks to assist side-channel attacks, the transformation of traces or the reduction of its length is advantageous to a certain extent.