Deep Learning Method for Power Side-Channel Analysis on Chip Leakages

Abstract

Power side channel analysis signal analysis is automated using deep learning. Signal processing and cryptanalytic techniques are necessary components of power side channel analysis. Chip leakages can be found using a classification approach called deep learning. In addition to this, we do this so that the deep learning network can automatically tackle signal processing difficulties such as re-alignment and noise reduction. We were able to break minimally protected Advanced Encryption Standard (AES), as well as masking-countermeasure AES and protected elliptic-curve cryptography (ECC). These results demonstrate that the attacker knowledge required for side channel analysis, which had previously placed a significant emphasis on human abilities, is decreasing. This research will appeal to individuals with a technical background who have an interest in deep learning, side channel analysis, and security.

Design of Deep Learning Technique Based Side Channel Attack Analysis for System on Chips

“…As an example, the amount of time required to carry out a successful attack as well as the amount of effort required to carry out the assault, often known as the degree of difficulty, are evaluated as part of the Common Criteria security evaluation of a device. According to [9], the total security grade of the chip is impacted by both of these criteria. In a nutshell, trust in security evaluation entails taking into consideration the most dangerous adversary, which has an effect on the attack tactics that are chosen [10].…”