MLMSA: Multilabel Multiside-Channel-Information Enabled Deep Learning Attacks on APUF Variants
MLMSA: Multilabel Multiside-Channel-Information Enabled Deep Learning Attacks on APUF Variants
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DOI：
10.1109/tcad.2023.3236563
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发表时间：
2022-07
影响因子：
2.9
通讯作者：
Yansong Gao;Jianrong Yao;Lihui Pang;Wei Yang;Anmin Fu;S. Al-Sarawi;Derek Abbott
中科院分区：
文献类型：
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作者：
Yansong Gao;Jianrong Yao;Lihui Pang;Wei Yang;Anmin Fu;S. Al-Sarawi;Derek Abbott
为了提高硅强物理不可克隆函数（puf）的建模弹性，特别是产生大量挑战响应对（CRPs）的APUF，已经设计了许多复合APUF变体，如XOR-APUF，中间puf (iPUF)，前馈APUF （FF-APUF）和OAX-APUF。当在建模弹性方面检查它们的安全性时，利用多个信息源，如电源侧信道信息（SCI）或/和可靠性SCI，考虑到一个挑战，这对它们在实践中假定的建模弹性提出了挑战。在多标签/头部深度学习（DL）模型架构的基础上，这项工作提出了多标签多侧通道信息支持的深度学习攻击（MLMSAs），以彻底评估上述APUF变体的建模弹性。尽管它很简单，MLMSA可以成功地破坏大规模的APUF变体，这是以前没有实现的。更准确地说，当CRPs、功率SCI和可靠性SCI同时使用时，MLMSA会打破128级30- xor - apuf、(9,9)-和(2,18)- ipufs和$(2,2,30)$ - oax - apuf。即使仅利用易于获得的可靠性SCI和crp，它也能破坏128级12-XOR-APUF和$(2,2,9)$ -OAX-APUF。128级六回路FF-APUF和单回路20-XOR-FF-APUF可以通过同时使用可靠性SCI和crp来断开。所有这些攻击通常在一个小时内用一台标准的个人电脑完成。因此，MLMSA是评估其他现有或任何新兴的强PUF设计的有用技术。
To improve the modeling resilience of silicon strong physical unclonable functions (PUFs), in particular, the APUFs that yield a very large number of challenge-response pairs (CRPs), a number of composited APUF variants, such as XOR-APUF, interpose-PUF (iPUF), feed-forward APUF (FF-APUF), and OAX-APUF, have been devised. When examining their security in terms of modeling resilience, utilizing multiple information sources, such as power side channel information (SCI) or/and reliability SCI, given a challenge is under-explored, which poses a challenge to their supposed modeling resilience in practice. Building upon multilabel/head deep learning (DL) model architecture, this work proposes multilabel multiside-channel-information-enabled DL attacks (MLMSAs) to thoroughly evaluate the modeling resilience of aforementioned APUF variants. Despite its simplicity, MLMSA can successfully break large-scaled APUF variants, which has not previously been achieved. More precisely, the MLMSA breaks 128-stage 30-XOR-APUF, (9, 9)- and (2, 18)-iPUFs, and $(2,2,30)$ -OAX-APUF when CRPs, power SCI, and reliability SCI are concurrently used. It breaks 128-stage 12-XOR-APUF and $(2,2,9)$ -OAX-APUF even when only the easy-to-obtain reliability SCI and CRPs are exploited. The 128-stage six-loop FF-APUF and one-loop 20-XOR-FF-APUF can be broken by simultaneously using reliability SCI and CRPs. All these attacks are normally completed within an hour with a standard personal computer. Therefore, MLMSA is a useful technique for evaluating other existing or any emerging strong PUF designs.