Deep Learning based Differential Distinguisher for Lightweight Cipher PRESENT
Recent years have seen a major involvement of deep learning architecture in the cryptanalysis of various lightweight ciphers. The present study is inspired by the work of Gohr and Baksi et al. in the field to develop a deep neural network-based differential distinguisher for round reduced PRESENT lightweight block cipher. We present a multi-layer perceptron network which can distinguish between 3-6 rounds of PRESENT cipher data and a randomly generated data with a significantly high probability. We also discuss the possible improvements in the original approach of the differential distinguisher presented by Baksi et al.