Active learning for fast and slow modeling attacks on Arbiter PUFs
Active learning for fast and slow modeling attacks on Arbiter PUFs
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DOI：
10.1109/dsd60849.2023.00045
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Vincent Dumoulin;Wenjing Rao;N. Devroye
中科院分区：
文献类型：
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作者：
Vincent Dumoulin;Wenjing Rao;N. Devroye
建模攻击，其中广告使用机器学习技术来建模基于硬件的物理无统治功能（PUF）对这些硬件安全性的可行性构成了极大的威胁。 （CRP）用作机器学习算法的标记数据，用于arbiter-puf，这是一个基于延迟的PUF权重（由于制造不完美），我们研究了主动学习在支持矢量机（SVM）中的作用，我们专注于挑战选择，以帮助SVM算法学习“快速”并学习“速度”。在先前的工作中，在挑战的样本中。有效，或者可能会在随意的挑战中询问PUF的CRP时，可能会形成更强大的攻击。放慢攻击者，这些攻击者仅限于偷听CRP。
Modeling attacks, in which an adversary uses machine learning techniques to model a hardware-based Physically Unclonable Function (PUF) pose a great threat to the viability of these hardware security primitives. In most modeling attacks, a random subset of challenge-response-pairs (CRPs) are used as the labeled data for the machine learning algorithm. Here, for the arbiter-PUF, a delay based PUF which may be viewed as a linear threshold function with random weights (due to manufacturing imperfections), we investigate the role of active learning in Support Vector Machine (SVM) learning. We focus on challenge selection to help SVM algorithm learn “fast” and learn “slow”. Our methods construct challenges rather than relying on a sample pool of challenges as in prior work. Using active learning to learn “fast” (less CRPs revealed, higher accuracies) may help manufacturers learn the manufactured PUFs more efficiently, or may form a more powerful attack when the attacker may query the PUF for CRPs at will. Using active learning to select challenges from which learning is “slow” (low accuracy despite a large number of revealed CRPs) may provide a basis for slowing down attackers who are limited to overhearing CRPs.