基于最大均值差异的能量侧信道泄露量化评估  

Quantitative Assessment of Power Side-Channel Leakage Based on MMD

作　　者：洪亮[1] 翟元洁 王嘉熙 郑健 胡伟[1] HONG Liang;ZHAI Yuan-Jie;WANG Jia-Xi;ZHENG Jian;HU Wei(School of Cyberspace Security,Northwestern Polytechnical University,Xi'an 710072)

机构地区：[1]西北工业大学网络空间安全学院,西安710072

出　　处：《计算机学报》2024年第6期1355-1371,共17页Chinese Journal of Computers

基　　金：国家重点研发计划“处理器集成电路设计脆弱性检测与形式化验证”(2021YFB3100901);国家自然科学基金“基于联合信息流分析的细粒度标准化硬件安全模型与度量研究”(62074131);航天772所“同芯计划”项目资助.

摘　　要：能量侧信道分析是通过对密码设备运行时的能量消耗进行分析,推导出运行时的操作及操作涉及的敏感中间值.对密码设备进行能量泄露量化评估是分析密码设备信息泄露程度的重要手段,目前主流的评估方案主要关注于能量迹上单个样本点的泄露,并未充分考虑高阶攻击模型下的泄露评估问题,对于采用掩码防御措施的密码芯片来说,一旦发生泄露,通常表现为多变量联合泄露,因此采用传统的单样本点方法进行泄露评估会存在假阴性的问题.本文研究多点联合泄露评估问题,引入最大均值差异方法,提取能量迹的多变量联合特征,构建基于最大均值差异的能量泄露量化评估模型,提供了一种有效的能量侧信道泄露量化评估方法.通过实现无防御对策和有防御对策的AES算法,使用DPA contest v2、ASCAD v1和自采能量迹数据集进行实验,结果表明,基于最大均值差异的泄露量化评估方法能够有效降低单样本点检测方法出现假阴性的风险,HAC、MTD和Bartlett-F检验的对照结果也进一步验证了该方法的有效性.Power side-channel analysis is aimed at extracting the internal operations and associated sensitive intermediate values of cryptographic devices from their power consumption patterns.Quantitatively assessing power leakage is essential for comprehending the extent of information leakage.However,current power leakage assessment approaches often focus primarily on a single leakage point,which may be inadequate for addressing the challenges posed by higher-order attack models.Additionally,cryptographic implementations utilizing masking countermeasures frequently exhibit leakage involving multiple variables,complicating detection using traditional single-point methods and leading to false negatives.To tackle this challenge,this study investigates multi-point joint leakage assessment by employing the Maximum Mean Discrepancy(MMD)method to extract the multivariate joint characteristics of power traces.The primary contribution of this paper is to assess the power-side channel leakage of AES by determining whether the distribution of power trajectory samples corresponding to two sets of keys is identical and quanti-tatively evaluating the degree of leakage in the encryption process of cryptographic devices.Firstly,the Maximum Mean Discrepancy,representing the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbert space(RKHS),is introduced as a side-channel evaluation metric derived from transfer learning.By calculating the difference between the distributions of power trace samples,it assesses the disparity in distribution between two sets of power trace samples to evaluate the security of cryptographic devices.Secondly,building upon MMD,the Side-Channel Leakage Assessment(MMD-SCLA)scheme is proposed,which integrates multiple-point joint leakage characteristics of power traces to comprehensively quantify device security.This approach addresses the shortcomings of TVLA's single-variable quantification assessment and reduces the risk of false negatives in TVLA.To demonstrate the e

关 键 词：能量侧信道 信息泄露 量化评估 最大均值差异 掩码 AES 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]