强物理不可克隆函数的侧信道混合攻击

Side-Channel Hybrid Attacks on Strong Physical Unclonable Function

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.

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.