Open AccessSide-channel Analysis using Deep Learning on Hardware Trojans
Author(s) -
J. Kokila,
S. Chithra,
N. Ramasubramanian
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1049/1/012018
Subject(s) - side channel attack , computer science , key (lock) , convolutional neural network , deep learning , channel (broadcasting) , trojan , artificial intelligence , trace (psycholinguistics) , pattern recognition (psychology) , power analysis , hardware trojan , multilayer perceptron , power (physics) , artificial neural network , algorithm , cryptography , computer security , computer network , physics , linguistics , philosophy , quantum mechanics
A power side-channel analysis is proposed to nd the correct key in hardware trojan affected AES by evaluating correlation between every sub-key guesses. A deep learning method for side-channel analysis (SCA) is proposed, which consists of two phases-characterization and attack. Multilayer perceptron (MLP) and convolutional neural networks(CNN) are used as the models to determine the SCA and their performances are evaluated. Two kinds of desynchronization were used, where maximum possible value with which the trace can be shifted was 0 in rst and 100 in the second. SCA is performed in these desynchronized traces also.