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.

翻译：侧信道分析（SCA）对现代计算系统的隐私和安全构成明确威胁。绝大多数通信通过密码算法进行保护。这些算法从密码学角度而言通常可证明是安全的，但其在真实硬件上的实现会引入漏洞。攻击者可利用这些漏洞实施SCA并恢复秘密密钥或内部状态等机密信息。随着机器学习（尤其是深度学习）增强型攻击日益普遍，SCA的威胁已大幅增加。本文将审视用于侧信道分析的最新深度学习技术、其背后的理论及实施方式。重点将聚焦于基于深度学习的轮廓化攻击，同时也会探讨一些由深度学习技术增强的新兴方法，例如非轮廓化攻击、人工迹线生成等。最后，我们将评估并比较针对ANSSI SCA数据库（ASCAD）的不同深度学习增强型SCA方案及其相对性能。这将为保护密码实现免受最新SCA攻击提供新的研究方向。