基于多源数据聚合的神经网络侧信道攻击
A Side Channel Attack Based on Multi-Source Data Aggregation Neural Network
张润莲 1潘兆轩 1李金林 1武小年 1韦永壮1
作者信息
- 1. 密码学与信息安全重点实验室(桂林电子科技大学) 广西桂林 541004
- 折叠
摘要
基于深度学习的侧信道攻击需要针对密码算法的每一个密钥字节建模并训练,数据采集和模型训练开销大.针对该问题,提出一种基于多源数据聚合的神经网络侧信道攻击方法.为筛选具有良好泛化效果的密钥字节泄露数据进行数据聚合,以AES-128算法为例,先基于 16个密钥字节的泄露数据训练 16个单密钥字节模型,分别实现对 16个密钥字节的恢复;其次,设计一种打分机制评估各单密钥字节模型的泛化效果,通过得分排序筛选出对各密钥字节恢复效果最好的单密钥字节模型;最后,以筛选模型所对应的各密钥字节泄露数据集构建多源数据聚合模型进行训练,实现密钥恢复.实验测试结果表明,多源数据聚合模型具有良好的泛化效果,有效提高了密钥恢复的准确率和效率,降低了恢复密钥所需的能量迹数量,其在采集能量迹较少的情况下依然具有较好的攻击效果.
Abstract
Side channel attack based on deep learning needs to model and train each key byte of the cryptographic algorithm,which costs a lot of data acquisition and model training.To solve this problem,a side channel attack method based on multi-source data aggregation neural network is proposed.In order to screen the leaked data of key byte with good generalization quality for data aggregation,taking AES-128 algorithm as an example,firstly 16 single key byte models are trained based on the leaked data of 16 key bytes,and models are used to recover 16 key bytes respectively.Secondly,a scoring mechanism is designed to evaluate the generalization effect of each single key byte model,and models with the best recovery effect for each key byte are selected according to score sorting.Finally,a multi-source data aggregation model is constructed based on the key byte leaked data sets corresponding to the selected models to realize key recovery.The tested results show that the multi-source data aggregation model has good generalization effect,effectively improves the accuracy and efficiency of key recovery,reduces the number of traces used to recover the key,and the model also has good attack effect in the case of less traces.
关键词
侧信道攻击/神经网络/打分机制/数据聚合/数据标签Key words
side channel attack/neural network/scoring mechanism/data aggregation/data labeling引用本文复制引用
基金项目
国家自然科学基金(62062026)
国家自然科学基金(61872103)
广西创新研究团队项目(2019GXNSFGA245004)
广西青年创新人才科研专项(桂科AD20238082)
广西自然科学基金(2020GXNSFBA297076)
广西壮族自治区研究生创新项目(2022YCXS082)
出版年
2024
NETL
NSTL
万方数据