Design and implementation of a physical security evaluation system for cryptographic chips based on machine learning

Side-channel attack is a commonly used attack method for recovering cryptographic chip keys, and plays an important role in the field of cryptographic chip physical security evaluation. Combining side-channel attacks with machine learning and replacing some steps of traditional side-channel attacks with machine learning methods can improve the efficiency of key-recovery from side-channel attacks to a certain extent. In practice, there is a problem that most existing cryptographic chip security evaluation systems cannot support the complete key recovery process, and fully improve the utilization of side information generated in the evaluation process. In this paper, we design a cryptographic chip physical security evaluation system based on machine learning. Through the integrated operation of power trace acquisition, preprocessing, analysis and evaluation, the correct key can be successfully recovered.