吴缙,,
李军伟,
曲彤洲,
董永兴
信息工程大学? ?郑州? ?450001
详细信息
作者简介:徐金甫：男，1965年生，教授，硕士生导师，研究方向为专用集成电路设计技术
吴缙：男，1994年生，硕士生，研究方向为专用集成电路设计技术
李军伟：男，1988年生，讲师，研究方向为安全芯片设计
曲彤洲：男，1994年生，硕士生，研究方向为可重构计算与信息安全
董永兴：男，1994年生，硕士生，研究方向为专用集成电路设计技术
通讯作者:吴缙 woshi57890@163.com
中图分类号:TP331计量
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被引次数:0
出版历程
收稿日期:2018-08-06
修回日期:2019-02-11
网络出版日期:2019-03-23
刊出日期:2019-07-01
Controlled Physical Unclonable Function Research Based on Sensitivity Confusion Mechanism
Jinfu XU,Jin WU,,
Junwei LI,
Tongzhou QU,
Yongxing DONG
The Information Engineering University, Zhengzhou 450001, China
摘要
摘要:为了克服物理不可克隆函数(PUF)面对建模攻击的脆弱性，该文提出一种基于敏感度混淆机制的控制型PUF架构。根据PUF的布尔函数定义及Walsh谱理论，推导出各个激励位具有不同敏感度，分析并归纳了与混淆值位宽奇偶性有关的位置选取规则。利用该规则指导了多位宽混淆算法(MWCA)的设计，构建了具有高安全性的控制型PUF架构。将基础PUF结构作为控制型PUF的防护对象进行实验评估，发现基于敏感度混淆机制的控制型PUF所产生的响应具有较好的随机性。采用逻辑回归算法对不同PUF结构进行建模攻击，实验结果表明，相比基本ROPUF、仲裁器PUF以及基于随机混淆机制的OB-PUF，基于敏感度混淆机制的控制型PUF能够显著提高PUF的抗建模攻击能力。
关键词:信息安全/
机器学习/
布尔函数/
敏感度
Abstract:In order to overcome the vulnerability of Physical Unclonable Function (PUF) to modeling attacks, a controlled PUF architecture based on sensitivity confusion mechanism is proposed. According to the Boolean function definition of PUF and Walsh spectrum theory, it is derived that each excitation bit has different sensitivity, and the position selection rules related to the parity of the confound value bit width are analyzed and summarized. This rule guides the design of the Multi-bit Wide Confusion Algorithm (MWCA) and constructs a controlled PUF architecture with high security. The basic PUF structure is evaluated as a protective object of the controlled PUF. It is found that the response generated by the controlled PUF based on the sensitivity confusion mechanism has better randomness. Logistic regression algorithm is used to model different PUF attack. The experimental results show that compared with the basic ROPUF, the arbiter PUF and the OB-PUF based on the random confusion mechanism, the controlled PUF based on the sensitivity confusion mechanism can significantly improve the PUF resistance capabilities for modeling attack.
Key words:Information security/
Machine learning/
Boolean function/
Sensitivity information
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