电子学报 ›› 2019, Vol. 47 ›› Issue (12): 2639-2646.DOI: 10.3969/j.issn.0372-2112.2019.12.025

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强物理不可克隆函数的侧信道混合攻击

刘威, 蒋烈辉, 常瑞   

  1. 战略支援部队信息工程大学网络空间安全学院, 河南郑州 450002
  • 收稿日期:2018-10-20 修回日期:2019-06-12 出版日期:2019-12-25 发布日期:2019-12-25
  • 作者简介:刘威 男,1982年1月出生于湖北省随州市.现为战略支援部队信息工程大学讲师.主要研究方向为嵌入式系统安全.E-mail:newwayclwy@sina.com;蒋烈辉 男,1967年5月出生于浙江省东阳市.现为战略支援部队信息工程大学教授、博士生导师.主要研究方向为计算机体系结构、逆向工程.E-mail:jiangliehui@163.com;常瑞 女,1981年10月出生于河南省郑州市.现为战略支援部队信息工程大学副教授、硕士生导师.主要研究方向为嵌入式系统安全.E-mail:crix1021@163.com
  • 基金资助:
    国家自然科学基金(No.61871405,No.61802431)

Side-Channel Hybrid Attacks on Strong Physical Unclonable Function

LIU Wei, JIANG Lie-hui, CHANG Rui   

  1. Institute of Cyberspace Security, Information Engineering University of Strategic Support Force, Zhengzhou, Henan 450002, China
  • Received:2018-10-20 Revised:2019-06-12 Online:2019-12-25 Published:2019-12-25
  • Supported by:
     

摘要: 物理不可克隆函数(Physical Unclonable Function,PUF)凭借其固有的防篡改、轻量级等特性,在资源受限的物联网安全领域拥有广阔的应用前景,其自身的安全问题也日益受到关注.多数强PUF可通过机器学习方法建模,抗机器学习的非线性结构PUF难以抵御侧信道攻击.本文在研究强PUF建模的基础上,基于统一符号规则分类介绍了现有的强PUF侧信道攻击方法如可靠性分析、功耗分析和故障注入等,重点论述了各类侧信道/机器学习混合攻击方法的原理、适用范围和攻击效果,文章最后讨论了PUF侧信道攻击面临的困境和宜采取的对策.

 

关键词: 强物理不可克隆函数, 侧信道混合攻击, 机器学习, 可靠性, 功耗分析, 故障注入

Abstract: Due to the tamperproof and lightweight nature,physical unclonable function are proposed to provide security with low cost for the internet of things.Security of PUF itself has attracted much more attention.Almost all strong PUF can be modeled using machine learning techniques,while the complicated PUF with non-linear structure,which are resistant to machine learning modeling,are vulnerable to side channel attacks.According to the unified symbol rules,the paper presents existing side channel attack methods on strong PUFs,such as reliability analysis,power analysis and fault injection.The principles,performance and applications of side channel/machine learning hybrid attack methods are elaborated and analyzed.In the end,the temporary predicaments and countermeasures of side channel attack on PUF are discussed.

Key words: strong PUF, side-channel hybrid attack, machine learning, reliability, power analysis, fault injection

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