Side-channel attacks represent a realistic and serious threat to the security of embedded devices for already almost three decades. A variety of attacks and targets they can be applied to have been introduced, and while the area of side-channel attacks and their mitigation is very well-researched, it is yet to be consolidated.

Deep learning-based side-channel attacks entered the field in recent years with the promise of more competitive performance and enlarged attackers’ capabilities compared to other techniques. At the same time, the new attacks bring new challenges and complexities to the domain, making the systematization of knowledge (SoK) even more critical.

We first dissect deep learning-based side-channel attacks according to the different phases they can be used in and map those phases to the efforts conducted so far in the domain. For each phase, we identify the weaknesses and challenges that triggered the known open problems. We also connect the attacks to the threat models and evaluate their advantages and drawbacks. Finally, we provide a number of recommendations to be followed in deep learning-based side-channel attacks.

近三年来，旁路攻击对嵌入式设备的安全构成了现实而严重的威胁。已经介绍了可以应用它们的各种攻击和目标，虽然侧信道攻击及其缓解措施的领域得到了很好的研究，但仍有待巩固。

近年来，基于深度学习的侧信道攻击进入该领域，与其他技术相比，有望提供更具竞争力的性能并扩大攻击者的能力。同时，新的攻击给领域带来了新的挑战和复杂性，使得知识系统化 (SoK)变得更加关键。

我们首先根据可以使用的不同阶段剖析基于深度学习的侧信道攻击，并将这些阶段映射到迄今为止在该领域所做的努力。对于每个阶段，我们都确定了引发已知开放问题的弱点和挑战。我们还将攻击与威胁模型联系起来并评估它们的优缺点。最后，我们提供了一些在基于深度学习的侧信道攻击中要遵循的建议。