Deep Learning-based Side Channel Attack on HMAC SM3

• Main Authors: Xin Jin, Yong Xiao, Shiqi Li, Suying Wang
• Format: Article
• Language: English
• Published: Universidad Internacional de La Rioja (UNIR) 2021-03-01
• Series: International Journal of Interactive Multimedia and Artificial Intelligence
• Subjects: convolution neural network; hmac; side channel analysis
• Online Access: https://www.ijimai.org/journal/bibcite/reference/2841

SM3 is a Chinese hash standard. HMAC SM3 uses a secret key to encrypt the input text and gives an output as the HMAC of the input text. If the key is recovered, adversaries can easily forge a valid HMAC. We can choose different methods, such as traditional side channel analysis, template attack-based side channel analysis to recover the secret key. Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis. In this paper, we try to recover the secret key with deep learning-based side channel analysis. We should train the network recursively for different parameters by using the same dataset and attack the target dataset with the trained network to recover different parameters. The experiment results show that the secret key can be recovered with deep learning-based side channel analysis. This work demonstrates the interests of this new method and show that this attack can be performed in practice.