基于卷积自编码器的侧信道分析
Side Channel Analysis Based on Convolutional Auto-encoder
曾钟静昕 1甘刚1
作者信息
- 1. 成都信息工程大学网络空间安全学院,四川 成都 610225
- 折叠
摘要
兴趣点作为侧信道分析中的重要指标,准确选择出有效的兴趣点具有重要意义.针对公钥密码算法的聚类模型中兴趣点选择效果不佳,导致低识别率的问题,本文提出一种基于卷积自编码器的兴趣点选择方法.该方法在数据预处理后使用卷积自编码器来对数据特征进行学习,将其编码输出作为选择的兴趣点,结合聚类算法来完成侧信道攻击,最终成功恢复出密钥.实验以SM2解密算法中的多倍点运算过程作为研究对象,结果显示本文提出的方法可以用于侧信道分析中数据的兴趣点选择,大大提高了神经网络在侧信道分析方面的灵活性和实用性.
Abstract
As an important indicator in side channel analysis,Points-Of-Interest(POI)is of great significance to accurately se-lect effective POI.Aiming at the problem of poor selection of POI in the clustering model of public key cryptography algorithm,which leads to low recognition rate,this paper proposes a method of POI selection based on convolutional auto-encoder.After data preprocessing,the method uses convolutional auto-encoder to learn data features,and uses its encoded output as the se-lected POI,combines with clustering algorithm to complete side channel attack,and finally successfully recoveres the key.The experiment takes the multi-point operation process in the SM2 decryption algorithm as the research object,and the results show that the proposed method can be used for data POI selection in the side channel analysis,which greatly improves the flexibility and practicality of neural networks in side channel analysis.
关键词
侧信道/卷积自编码器/聚类/SM2算法/兴趣点选择Key words
side channel/convolutional auto-encoder/clustering/SM2 algorithm/POI selection引用本文复制引用
基金项目
四川省科技计划(2021ZYD0011)
四川省科技计划(23ZDYF0380)
四川省哲学社会科学规划项目(SC21B034)
成都信息工程大学创新创业训练计划(20201062125)
成都信息工程大学创新创业训练计划(202110621224)
成都信息工程大学创新创业训练计划(202110621225)
出版年
2024
国家科技期刊平台
NSTL
万方数据