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Machine learning resistant strong PUF: Possible or a pipe dream?

机译：耐机器学习能力强的PUF：可能还是梦想？

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摘要

Physically unclonable functions (PUFs) are emerging as hardware primitives for key-generation and light-weight authentication. Strong PUFs represent a variant of PUFs which respond to a user challenge with a response determined by its unique manufacturing process variations. Unfortunately many of the Strong PUFs have been shown to be vulnerable to model building attacks when an attacker has access to challenge and response pairs. In mounting a model building attack, typically machine learning is used to build a software model to forge the PUF. Researchers have long been interested in designing Strong PUFs that are resistant to model building attacks. However, with innovations in application of machine learning, nearly all Strong PUFs presented in the literature have been broken. In this paper, first we present results from a set of experiments designed to show that if certain randomness properties can be met, cascaded structure based Strong PUFs can indeed be made machine learning (ML) attack resistant against known ML attacks. Next we conduct machine learning experiments on an abstract PUF model using Support Vector Machines, Logistic Regression, Bagging, Boosting and Evolutionary techniques to establish criteria for machine learning resistant Strong PUF design. This paper does not suggest how to harvest the process variation, which remains within the purview of a circuit designer; rather it suggests what properties of the building blocks to aim for towards building a machine learning resistant Strong PUF ¿¿¿ thus paving the path for a systematic design approach.
机译：物理上不可克隆的功能（PUF）逐渐成为用于密钥生成和轻量级身份验证的硬件原语。强大的PUF代表了PUF的一种变体，可以通过其独特的制造工艺变化来确定对用户挑战的响应。不幸的是，当攻击者可以访问质询和响应对时，许多强大的PUF被证明容易遭受模型构建攻击。在发起模型构建攻击时，通常使用机器学习来构建软件模型来伪造PUF。长期以来，研究人员一直对设计可抵抗模型构建攻击的功能强大的PUF感兴趣。但是，随着机器学习应用程序的创新，文献中提出的几乎所有Strong PUF都被破坏了。在本文中，我们首先提供一组实验结果，这些实验旨在证明如果可以满足某些随机性，基于层叠结构的Strong PUF确实可以使机器学习（ML）抵御已知的ML攻击。接下来，我们使用支持向量机，Logistic回归，装袋，提升和进化技术在抽象PUF模型上进行机器学习实验，以建立针对机器学习的强PUF设计标准。本文没有建议如何收集工艺变化，而这仍然是电路设计人员的职责范围。而是提出了构建耐机器学习性强PUF的构建基块的哪些属性，从而为系统设计方法铺平了道路。

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