Mc-PUF:基于内存和机器学习的物联网设备认证弹性强PUF

Phillip Williams, Haytham Idriss, M. Bayoumi
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引用次数: 3

摘要

物理不可克隆函数(puf)是基于硬件的安全原语,它利用制造过程的变化来实现二进制密钥(弱puf)或二进制函数(强puf)。由于其低功耗和低面积开销,这种原语对于受限设备中的密钥生成和身份验证是理想的。然而,近年来许多研究论文都集中在puf对建模攻击的脆弱性上。这种攻击是可能的,因为puf挑战和响应交换通常是在没有加密的通信通道上传输的。因此,攻击者可以收集挑战-响应对,并将其用作学习算法的输入,以创建一个模型,该模型可以预测给定新挑战的响应。在本文中,我们介绍了一种串行和并行的新型64位基于内存的受控PUF (Mc-PUF)架构,该架构用于设备认证,具有高唯一性和随机性,可靠和抗建模攻击的弹性。这些架构通过利用从具有控制逻辑的同步随机存取存储器(SRAM) PUF的指纹中提取的位来产生响应。串行结构的合成面积为1.136K GE,并行结构的合成面积为3.013K GE。从建模攻击中获得的最佳预测精度为~50%,这可以防止攻击者准确预测对未来挑战的响应。我们还通过XOR-ing几个mc - puf展示了设计的可扩展性,进一步提高了其安全性和性能。本文的其余部分介绍了所建议的体系结构,以及它们的硬件实现、面积和功耗,以及针对建模攻击的安全弹性。3-XOR Mc-PUF有最大的开销,但它产生了最好的随机性、唯一性和抗建模攻击的弹性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mc-PUF: Memory-based and Machine Learning Resilient Strong PUF for Device Authentication in Internet of Things
Physically Unclonable Functions (PUFs) are hardware-based security primitives that utilize manufacturing process variations to realize binary keys (Weak PUFs) or binary functions (Strong PUFs). This primitive is desirable for key generation and authentication in constrained devices, due to its low power and low area overhead. However, in recent years many research papers are focused on the vulnerability of PUFs to modeling attacks. This attack is possible because the PUFs challenge and response exchanges are usually transmitted over communication channel without encryption. Thus, an attacker can collect challenge-response pairs and use it as input into a learning algorithm, to create a model that can predict responses given new challenges. In this paper we introduce a serial and a parallel novel 64-bits memory-based controlled PUF (Mc-PUF) architecture for device authentication that has high uniqueness and randomness, reliable, and resilient against modeling attacks. These architectures generate a response by utilizing bits extracted from the fingerprint of a synchronous random-access memory (SRAM) PUF with a control logic. The synthesis of the serial architecture yielded an area of 1.136K GE, while the parallel architecture was 3.013K GE. The best prediction accuracy obtained from the modeling attack was ~50%, which prevents an attacker from accurately predicting responses to future challenges. We also showcase the scalability of the design through XOR-ing several Mc-PUFs, further improving upon its security and performance. The remainder of the paper presents the proposed architectures, along with their hardware implementations, area and power consumption, and security resilience against modeling attacks. The 3-XOR Mc-PUF had the greatest overhead, but it produced the best randomness, uniqueness, and resilience against modeling attacks.
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