The last decade has witnessed remarkable research advances at the intersection of machine learning (ML) and hardware security. The confluence of the two technologies has created many interesting and unique opportunities, but also left some issues in their wake. ML schemes have been extensively used to enhance the security and trust of embedded systems like hardware Trojans and malware detection. On the other hand, ML-based approaches have also been adopted by adversaries to assist side-channel attacks, reverse engineer integrated circuits and break hardware security primitives like Physically Unclonable Functions (PUFs). Deep learning is a subfield of ML. It can continuously learn from a large amount of labeled data with a layered structure. Despite the impressive outcomes demonstrated by deep learning in many application scenarios, the dark side of it has not been fully exposed yet. The inability to fully understand and explain what has been done within the super-intelligence can turn an inherently benevolent system into malevolent. Recent research has revealed that the outputs of Deep Neural Networks (DNNs) can be easily corrupted by imperceptibly small input perturbations. As computations are brought nearer to the source of data creation, the attack surface of DNN has also been extended from the input data to the edge devices. Accordingly, due to the opportunities of ML-assisted security and the vulnerabilities of ML implementation, in this paper, we will survey the applications, vulnerabilities and fortification of ML from the perspective of hardware security. We will discuss the possible future research directions, and thereby, sharing a roadmap for the hardware security community in general.

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同一枚硬币的两面：硬件安全中机器学习的利与弊

过去十年见证了机器学习 (ML) 和硬件安全交叉领域的显着研究进展。这两种技术的融合创造了许多有趣和独特的机会，但也留下了一些问题。ML 方案已被广泛用于增强嵌入式系统（如硬件木马和恶意软件检测）的安全性和信任度。另一方面，攻击者也采用基于 ML 的方法来协助侧信道攻击、对集成电路进行逆向工程并破坏物理不可克隆功能 (PUF) 等硬件安全原语。深度学习是机器学习的一个子领域。它可以从大量具有分层结构的标记数据中不断学习。尽管深度学习在许多应用场景中展示了令人印象深刻的成果，它的阴暗面还没有完全暴露出来。无法完全理解和解释超级智能内部所做的事情可能会将一个固有的仁慈系统变成恶意系统。最近的研究表明，深度神经网络 (DNN) 的输出很容易被难以察觉的小输入扰动破坏。随着计算更接近数据创建的源头，DNN 的攻击面也已从输入数据扩展到边缘设备。因此，由于 ML 辅助安全的机会和 ML 实现的漏洞，在本文中，我们将从硬件安全的角度调查 ML 的应用程序、漏洞和防御工事。我们将讨论未来可能的研究方向，从而，