基于深度学习LSTM的侧信道分析

doi:10.19678/j.issn.1000-3428.0059210

计算机工程 ›› 2021, Vol. 47 ›› Issue (10): 140-146. doi: 10.19678/j.issn.1000-3428.0059210

基于深度学习LSTM的侧信道分析

王俊年^1,3, 朱斌^1,3, 于文新^2,3, 王皖^1,3, 胡钒梁^1,3

1. 湖南科技大学物理与电子科学学院, 湖南湘潭 411201;
2. 湖南科技大学信息与电气工程学院, 湖南湘潭 411201;
3. 知识处理与网络化制造湖南省普通高等学校重点实验室, 湖南湘潭 411201

收稿日期:2020-08-10 修回日期:2020-10-21 发布日期:2020-11-05
作者简介:王俊年(1968-),男,教授、博士生导师,主研方向为智能信息处理;朱斌,硕士研究生;于文新,讲师、博士;王皖、胡钒梁,硕士研究生。
基金资助:
国家自然科学基金（61973109）。

Side Channel Analysis Attack Based on Deep Learning LSTM

WANG Junnian^1,3, ZHU Bin^1,3, YU Wenxin^2,3, WANG Wan^1,3, HU Fanliang^1,3

1. School of Physics and Electronic Science, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China;
2. School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China;
3. Key Laboratory of Knowledge Processing and Networked Manufacturing, College of Hunan Province, Xiangtan, Hunan 411201, China

Received:2020-08-10 Revised:2020-10-21 Published:2020-11-05

摘要/Abstract

摘要： 加密数据的安全性受到加密算法和加密设备的影响，为评估密码硬件的可靠性，能量分析等多种针对加密平台的攻击方法得到广泛应用。深度学习是一种性能良好的数据分析方法，基于深度学习的功耗侧信道攻击方法一经提出便引起关注。提出一种基于深度学习LSTM的侧信道攻击方法，利用相关功耗分析方法确定侧信道功耗数据的兴趣点，通过兴趣点位置选择合适的兴趣区间作为特征向量以搭建神经网络模型。实验结果表明，相比MLP和CNN模型，LSTM网络模型在侧信道攻击中具有较高的攻击效率。

关键词: 能量分析, 侧信道攻击, 深度学习, 相关功耗分析, 长短时记忆网络

Abstract: The security of encrypted data is affected by encryption algorithms and encryption devices.The reliability of the encryption devices can be tested by using multiple types of attacks, such as energy analysis.Among different attack methods, the method of side channel attacks based on deep learning has been widely concerned since it was proposed. This paper proposes a side channel attack method based on a deep learning network, LSTM.The method employs Correlation Power Analysis(CPA) to determine the interest points of the side channel power consumption data.Then based on the position of the interest points, an appropriate interest interval is selected as the feature vector to build the neural network model.The experimental results show that the LSTM model has higher efficiency in implementing side channel attacks than MLP and CNN.

Key words: power analysis, side channel attack, deep learning, Correlation Power Analysis(CPA), Long Short-Term Memory(LSTM) network

中图分类号:

TP391

王俊年, 朱斌, 于文新, 王皖, 胡钒梁. 基于深度学习LSTM的侧信道分析[J]. 计算机工程, 2021, 47(10): 140-146.

WANG Junnian, ZHU Bin, YU Wenxin, WANG Wan, HU Fanliang. Side Channel Analysis Attack Based on Deep Learning LSTM[J]. Computer Engineering, 2021, 47(10): 140-146.

https://www.ecice06.com/CN/Y2021/V47/I10/140

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