Test Vector Leakage Assessment Technique of Side-channel Power Information
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摘要: 侧信道能量分析攻击技术以其计算复杂度低和通用性强等优势，给各类密码产品带来了严峻的安全挑战。抗能量分析攻击能力的评估已经成为密码产品安全性测评的重要环节。测试向量泄漏评估(TVLA)是一种基于假设检验的能量信息泄漏评估方法，具有简单高效和可操作性强等特点，目前被广泛应用于密码产品的安全性评估实验中。为全面把握TVLA技术机理及研究现状，该文首先对TVLA技术进行了概述，阐述了其实现原理并介绍了其实施过程，紧接着对特定和非特定两种TVLA的优势与不足进行了对比，随后参考已有研究，对TVLA的局限性进行了深入分析和归纳，在此基础上重点介绍并分析了已有的TVLA的改进方法，最后对TVLA未来可能的发展方向进行了展望。Abstract: The side-channel power analysis attack technique, with its advantages of low computational complexity and high generality, poses a critical security challenge to all kinds of cryptographic implementations. The assessment of resistance to power analysis attacks has become an essential aspect of cryptographic product security evaluation. Test Vector Leakage Assessment (TVLA) is a power information leakage evaluation method based on hypothesis testing techniques, which is highly efficient and operable, and is now widely used in security evaluation experiments of cryptographic products. In order to have a comprehensive understanding of the mechanism of TVLA technology and the current status of research, this paper begins with an overview of TVLA technology, including an explanation of its implementation principles and a description of its implementation process, followed by a comparison of the advantages and disadvantages of both specific and non-specific TVLA. The limitations of TVLA are then analyzed and summarized in depth with reference to existing studies, based on which existing approaches for improving TVLA are highlighted and analyzed, and finally the possible future directions of TVLA are prospected.
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表 1 特定和非特定TVLA的对比
优势 不足 特定TVLA 针对DPA等常用攻击的测试效果较好 对能耗数据分组时需计算算法中间值；可供选择的算法中间值过多，难以保证测试的全面性 非特定TVLA 对能耗数据的分组较为简便，测试结果较为全面 所选择的测试向量对结果影响较大，需使用不同的测试向量重复实施评估 
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表 2 TVLA改进方法汇总表
所针对问题 对应文献 主要方法 优缺点(研究意义) TVLA对高阶和多变量信息
泄漏容易产生漏检[25] Hotelling’s T2检验 能够提高多变量泄漏的检出率，但计算复杂度高 [26] 增量算法 适用于多变量和高阶泄漏，效率较高 [27] 多分类F检验和Bartlett检验 2阶以内的泄漏检测准确率较高 [28] KS检验 鲁棒性较强 [29] 统计直方图 效率较高，但初始化较繁琐 TVLA检验统计量t值的
参考意义有限[30] 理论推导和实验验证结合 建立了TVLA结果、信噪比和能量分析攻击成功率之间的联系 [32] 回归模型 回答TVLA所检测出的泄漏是否可以利用的问题 TVLA对能耗数据的信噪
比要求较高[22] 快速傅里叶变换 减小了能量迹未对齐对TVLA结果的影响 [33] 多源时频信息融合 避免了对齐和降噪的预处理步骤，检测效率和准确率较高 [34] 配对t检验 统计结果较稳定准确 [35] 相关关系 进一步优化了文献[34]中的方法 [36] 深度学习 不必考虑能量迹是否对齐和泄漏的统计矩阶数等问题，
且涵盖了多变量的
泄漏情形；但所需时间成本较大，存在过拟合等问题TVLA犯误判错误的概率随能量迹
中采样点数量增加而增大[37] 将t值的阈值设置为5 导致犯假阴性误判错误的概率增加 [38] HC检验 能够有效控制TVLA因仅依赖于单个采样点的
t值而犯误判错误的概率泄漏可能被隐藏在TVLA的
某个分组中[39] 卡方检验 可以和t检验结合使用以提高评估的准确性
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