A Review and Comparison of AI Enhanced Side Channel Analysis
Max Panoff Honggang Yu Haoqi Shan Yier Jin
Side Channel Analysis (SCA) presents a clear threat to privacy and security
in modern computing systems. The vast majority of communications are secured
through cryptographic algorithms. These algorithms are often provably-secure
from a cryptographical perspective, but their implementation on real hardware
introduces vulnerabilities. Adversaries can exploit these vulnerabilities to
conduct SCA and recover confidential information, such as secret keys or
internal states. The threat of SCA has greatly increased as machine learning,
and in particular deep learning, enhanced attacks become more common. In this
work, we will examine the latest state-of-the-art deep learning techniques for
side channel analysis, the theory behind them, and how they are conducted. Our
focus will be on profiling attacks using deep learning techniques, but we will
also examine some new and emerging methodologies enhanced by deep learning
techniques, such as non-profiled attacks, artificial trace generation, and
others. Finally, different deep learning enhanced SCA schemes attempted against
the ANSSI SCA Database (ASCAD) and their relative performance will be evaluated
and compared. This will lead to new research directions to secure cryptographic
implementations against the latest SCA attacks.