As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage. While the security features promised by PUFs are highly attractive for secure system designers, they have been shown to be vulnerable to various sophisticated attacks - most notably Machine Learning (ML) based modelling attacks (ML-MA) which attempt to digitally clone the PUF behaviour and thus undermine their security. More recent ML-MA have even exploited publicly known helper data required for PUF error correction in order to predict PUF responses without requiring knowledge of response data. In response to this, research is beginning to emerge regarding the authentication of PUF devices with the assistance of ML as opposed to traditional PUF techniques of storage and comparison of pre-known Challenge-Response pairs (CRPs). In this article, we propose a classification system using ML based on a novel `PUF-Phenotype' concept to accurately identify the origin and determine the validity of noisy memory derived (DRAM) PUF responses as an alternative to helper data-reliant denoising techniques. To our best knowledge, we are the first to perform classification over multiple devices per model to enable a group-based PUF authentication scheme. We achieve up to 98\% classification accuracy using a modified deep convolutional neural network (CNN) for feature extraction in conjunction with several well-established classifiers. We also experimentally verified the performance of our model on a Raspberry Pi device to determine the suitability of deploying our proposed model in a resource-constrained environment.
翻译:随着现代世界对高度安全和可靠轻便系统的需求增加,物理上不可测的功能(PUF)继续承诺以轻量的替代方法替代高成本加密技术和确保钥匙储存。尽管PUF承诺的安全特征对安全系统设计师非常吸引,但事实证明,这些安全特征很容易受到各种复杂袭击的影响,尤其是基于基于基于机械学习的模型袭击(ML-MA),这些袭击试图以数字方式克隆PUF行为,从而破坏其安全。最近的ML-MA甚至利用了众所周知的PUF错误校正所需的帮助者数据,以便预测PUF的反应,而不需要了解反应数据。对此,在ML的协助下,PUF承诺的安全特征对安全系统设计者非常有吸引力,但与传统的PUF储存和比较技术相比,他们很容易受到各种复杂的攻击。在本篇文章中,我们提议使用一个基于模型的“PUF-PHI-F-Phenorole”概念分类系统,以准确性地确定PUF(DRAM) 生成的冷实记忆(DRIM) 的响应者的反应,这是我们通过多级的精确度数据分类系统进行最佳的校正解环境分类。