Research on security architecture of strong PUF by adversarial learning

Yan LI; Wei LIU; Yuanlu SUN

doi:10.11959/j.issn.2096-109x.2021019

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Research on security architecture of strong PUF by adversarial learning

Papers | 更新时间：2024-06-03

- Research on security architecture of strong PUF by adversarial learning
- Chinese Journal of Network and Information Security Vol. 7, Issue 3, Pages: 115-122(2021)
- 作者机构：
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- DOI：10.11959/j.issn.2096-109x.2021019
  CLC： TP393
- Online First：2021-06，
  
  Published：15 June 2021
- 稿件说明：
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Yan LI, Wei LIU, Yuanlu SUN. Research on security architecture of strong PUF by adversarial learning[J]. Chinese Journal of Network and Information Security, 2021, 7(3): 115-122.
DOI：

Yan LI, Wei LIU, Yuanlu SUN. Research on security architecture of strong PUF by adversarial learning[J]. Chinese Journal of Network and Information Security, 2021, 7(3): 115-122. DOI： 10.11959/j.issn.2096-109x.2021019.

摘要

针对强物理不可复制函数（PUF，physical unclonable function）面临的机器学习建模威胁，基于对抗学习理论建立了强PUF的对抗机器学习模型，在模型框架下，通过对梯度下降算法训练过程的分析，明确了延迟向量权重与模型预测准确率之间的潜在联系，设计了一种基于延迟向量权重的对抗样本生成策略。该策略与传统的组合策略相比，将逻辑回归等算法的预测准确率降低了5.4%~9.5%，低至51.4%。结合资源占用量要求，设计了新策略对应的电路结构，并利用对称设计和复杂策略等方法对其进行安全加固，形成了ALPUF（adversarial learning PUF）安全结构。ALPUF不仅将机器学习建模的预测准确率降低至随机预测水平，而且能够抵御混合攻击和暴力破解。与其他 PUF结构的对比表明，ALPUF在资源占用量和安全性上均具有明显优势。

Abstract

To overcome the vulnerability of strong physical unclonable function

the adversarial learning model of strong PUF was presented based on the adversarial learning theory

then the training process of gradient descent algorithm was analyzed under the framework of the model

the potential relationship between the delay vector weight and the prediction accuracy was clarified

and an adversarial sample generation strategy was designed based on the delay vector weight.Compared with traditional strategies

the prediction accuracy of logistic regression under new strategy was reduced by 5.4% ~ 9.5%

down to 51.4%.The physical structure with low overhead was designed corresponding to the new strategy

which then strengthened by symmetrical design and complex strategy to form a new PUF architecture called ALPUF.ALPUF not only decrease the prediction accuracy of machine learning to the level of random prediction

but also resist hybrid attack and brute force attack.Compared with other PUF security structures

ALPUF has advantages in overhead and security.

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Keywords

references

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