计算机科学
混淆
机器学习
人工智能
物理不可克隆功能
卷积神经网络
架空(工程)
集合(抽象数据类型)
对抗性机器学习
人工神经网络
支持向量机
恶意软件
可靠性(半导体)
深度学习
计算机安全
密码学
操作系统
物理
功率(物理)
量子力学
程序设计语言
作者
Jiliang Zhang,C. N. Shen
出处
期刊:Cornell University - arXiv
日期:2018-06-06
被引量:1
标识
DOI:10.48550/arxiv.1806.02011
摘要
Strong physical unclonable function (PUF) is a promising solution for device authentication in resourceconstrained applications but vulnerable to machine learning attacks. In order to resist such attack, many defenses have been proposed in recent years. However, these defenses incur high hardware overhead, degenerate reliability and are inefficient against advanced machine learning attacks such as approximation attacks. In order to address these issues, we propose a Random Set-based Obfuscation (RSO) for Strong PUFs to resist machine learning attacks. The basic idea is that several stable responses are derived from the PUF itself and pre-stored as the set for obfuscation in the testing phase, and then a true random number generator is used to select any two keys to obfuscate challenges and responses with XOR operations. When the number of challenge-response pairs (CRPs) collected by the attacker exceeds the given threshold, the set will be updated immediately. In this way, machine learning attacks can be prevented with extremely low hardware overhead. Experimental results show that for a 64x64 Arbiter PUF, when the size of set is 32 and even if 1 million CRPs are collected by attackers, the prediction accuracies of Logistic regression, support vector machines, artificial neural network, convolutional neural network and covariance matrix adaptive evolutionary strategy are about 50% which is equivalent to the random guessing.
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