基于对抗学习的强PUF安全结构研究

李艳; 刘威; 孙远路

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

您当前的位置：

首页 >

文章列表页 >

基于对抗学习的强PUF安全结构研究

学术论文 | 更新时间：2024-06-03

- 基于对抗学习的强PUF安全结构研究
- Research on security architecture of strong PUF by adversarial learning
- 网络与信息安全学报 2021年7卷第3期页码：115-122
- 作者机构：
- 作者简介：
  
  [ "李艳（1988- ）女，河南郑州人，信息工程大学讲师，主要研究方向为网络空间安全" ]
  [ "刘威（1982- ）男，湖北随州人，信息工程大学讲师，主要研究方向为硬件安全" ]
  [ "孙远路（1974- ）男，河南商丘人，信息工程大学副教授，主要研究方向为管理科学与工程" ]
- 基金信息：
  
  国家自然科学基金;The National Natural Science Foundation of China(61871405);国家自然科学基金;The National Natural Science Foundation of China(61802431)
- DOI：10.11959/j.issn.2096-109x.2021019
  中图分类号： TP393
- 网络首发：2021-06，
  
  纸质出版：2021-06-15
- 稿件说明：
移动端阅览
李艳, 刘威, 孙远路. 基于对抗学习的强PUF安全结构研究[J]. 网络与信息安全学报, 2021,7(3):115-122.

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.
李艳, 刘威, 孙远路. 基于对抗学习的强PUF安全结构研究[J]. 网络与信息安全学报, 2021,7(3):115-122. DOI： 10.11959/j.issn.2096-109x.2021019.

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.

关键词

Keywords

references

SAHOO D P , NGUYEN P H , MUKHOPADHYAY D , et al . A case of lightweight PUF constructions:cryptanalysis and machine learning attacks [J ] . IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2015 , 34 ( 8 ): 1334 - 1343 .

LIU Y , XIE Y , BAO C , et al . A combined optimization-theoretic and side-channel approach for attacking strong physical unclonable functions [J ] . IEEE Transactions on Very Large Scale Integration Systems , 2017 , 26 ( 1 ): 73 - 81 .

RÜHRMAIR U , SÖLTER J , SEHNKE F , et al . PUF modeling attacks on simulated and silicon data [J ] . IEEE Transactions on Information Forensics ＆ Security , 2013 , 8 ( 11 ): 1876 - 1891 .

YE J , GUO Q , HU Y , et al . Modeling attacks on strong physical unclonable functions strengthened by random number and weak PUF [C ] // 2018 IEEE 36th VLSI Test Symposium . 2018 : 1 - 6 .

DELVAUX J . Machine-learning attacks on PolyPUFs,OB-PUFs,RPUFs,LHS-PUFs,and PUF–FSMs [J ] . IEEE Transactions on Information Forensics and Security , 2019 , 14 ( 8 ): 2043 - 2058 .

宋蕾 , 马春光 , 段广晗 . 机器学习安全及隐私保护研究进展 [J ] . 网络与信息安全学报 , 2018 , 4 ( 8 ): 1 - 11 .

SONG L , MA C G , DUAN G H . Machine learning security and privacy:a survey [J ] . Chinese Journal of Network and Information Security , 2018 , 4 ( 8 ): 1 - 11 .

BIGGIO B , NELSON B , LASKOV P . Poisoning attacks against support vector machines [C ] // ICML'12 2012 . 14671 - 1474 .

JAGIELSKI M , OPREA A , BIGGIO B , et al . Manipulating machine learning:poisoning attacks and countermeasures for regression learning [C ] // 2018 IEEE Symposium on Security and Privacy . 2018 . 19 - 35 .

BIGGIO B , ROLI F . Wild patterns:ten years after the rise of adversarial machine learning [J ] . Pattern Recognition , 2018 , 84 : 317 - 331 .

BECKER G T , . The gap between promise and reality:on the insecurity of XOR arbiter PUFs [C ] // Cryptographic Hardware and Embedded Systems . 2015 : 535 - 555 .

WANG S J , CHEN Y S , KATHERINE L , et al . Adversarial attack against modeling attack on PUFs [C ] // DAC2019 . 2019 : 1 - 6 .

PRADO H , PATIL C . Defeating strong PUF modeling attack via adverse selection of challenge-response pairs [C ] // AsianHOST2018 . 2018 : 25 - 30 .

XU X , MAHMOUD A , MAJZOOBI M , et al . Efficient power and timing side channels for physical unclonable functions [C ] // International Workshop on Cryptographic Hardware and Embedded Systems . 2014 : 476 - 492 .

GAO Y , LI G , MA H , et al . Obfuscated challenge-response:a secure lightweight authentication mechanism for PUF-based pervasive devices [C ] // Workshop in Conjunction with IEEE International Conference on Pervasive Computing and Communications . 2016 : 1 - 6 .

JING Y , YU H , LI X . RPUF:physical unclonable function with randomized challenge to resist modeling attack [C ] // Hardware-oriented Security ＆ Trust . 2016 : 1 - 6 .

徐金甫 , 吴缙 , 李军伟 . 基于敏感度混淆机制的控制型物理不可复制函数研究 [J ] . 电子与信息学报 , 2019 , 41 ( 7 ): 1601 - 1609 .

XU J F , WU J , LI J W . Research on the control physical non-clonable function based on sensitivity cofounding mechanism [J ] . Journal of Electronics and Information Technology , 2019 , 41 ( 7 ): 1601 - 1609 .

浏览量

435

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据