Research on Side Channel Attack Based on CNN-MGU

doi:10.3969/j.issn.1671-1122.2022.08.007

Abstract

Abstract:

Side-channel attacks based on deep learning pose a serious threat to the security of cryptographic algorithms and become a research hotspot. At present, some network models have problems such as low accuracy, poor robustness, and slow convergence. Aimed at these problems, this paper proposed a neural network model based on CNN-MGU by combining the advantages of convolutional neural network (CNN) and minimal gated unit (MGU). Firstly, the key information was effectively extracted by the CNN layer. Secondly, the timing dependency was fully learned by the MGU layer, and the key was recovered by dividing and conquering. Thirdly, the performance of the model was verified on fully synchronization and asynchronous traces. The experimental results show that compared with attack based on CNN and long short-term memory(LSTM) network, the accuracy of method based on CNN-MGU is improved by about 5.6% and 3.4% respectively. When the amount of jitter added in the data set increases from 0 to 50 and 100 respectively, the accuracy of the CNN-MGU is still 90% accuracy, which has strong robustness and fast convergence speed.

Key words: side channel attack, deep learning, CNN, MGU

CLC Number:

TP309

GAO Bo, CHEN Lin, YAN Yingjian. Research on Side Channel Attack Based on CNN-MGU[J]. Netinfo Security, 2022, 22(8): 55-63.

Figures/Tables 18

References 18

[1]	KOCHER P, JAFFE J, JUN B. Differential Power Analysis[C]//Springer. Annual International Cryptology Conference. Heidelberg: Springer, 1999: 388-397.
[2]	KOCHER P C. Timing Attacks on Implementations of Diffie-Hellman, RSA, DES, and other Systems[C]//Springer. 16th Annual International Cryptology Conference. Heidelberg: Springer, 1996: 104-113.
[3]	MARTINASEK Z, ZEMAN V. Innovative Method of the Power Analysis[J]. Radio Engineering, 2013, 22(2): 586-594.
[4]	MAGHREBI H, PORTIGLIATTI T, PROUFF E. Breaking Cryptographic Implementations Using Deep Learning Techniques in Security, Privacy, and Applied Cryptography Engineering [C]//Springer. Security, Privacy, and Applied Cryptography Engineering (SPACE 2016). Heidelberg: Springer, 2016: 3-26.
[5]	CAGLI E, DUMAS C, PROUFF E. Convolutional Neural Networks with Data Augmentation against Jitter-Based Countermeasures[C]//Springer. 19th International Conference (CHES 2017), Heidelberg: Springer, 2017: 45-68.
[6]	SAMIOTIS I P. Side-Channel Attacks Using Convolutional Neural Networks: A Study on the Performance of Convolutional Neural Networks on Side-Channel Data[J]. Frontiers in Artificial Intelligence, 2018, 3(1): 73-88. doi: 10.3389/frai.2020.00073 URL
[7]	WANG Kai, YAN Yingjian, GUO Pengfei, et al. Research on Power Analysis Attack Based on Improved Residual Network and Data Augmentation Technology[J]. Journal of Cryptologic Research, 2020, 7(4): 551-564. doi: 10.13868/j.cnki.jcr.000389
	王恺, 严迎建, 郭朋飞, 等. 基于改进残差网络和数据增强技术的能量分析攻击研究[J]. 密码学报, 2020, 7(4): 551-564. doi: 10.13868/j.cnki.jcr.000389
[8]	CHEN Ping, WANG Ping, DONG Gaofeng, et al. SincNet-Based Side Channel Attack[J]. Journal of Cryptography, 2020, 7(5): 583-594.
	陈平, 汪平, 董高峰, 等. 基于SincNet的侧信道攻击[J]. 密码学报, 2020, 7(5): 583-594. doi: 10.13868/j.cnki.jcr.000391
[9]	WANG Junnian, ZHU Bin, YU Wenxin, et al. Side Channel Analysis Based on Deep Learning LSTM[J]. Journal of Computer Engineering, 2021, 47(10): 140-146.
	王俊年, 朱斌, 于文新, 等. 基于深度学习LSTM的侧信道分析[J]. 计算机工程, 2021, 47(10): 140-146.
[10]	WANG Ping, ZHENG Mengce, NAN Jiehui, et al. A Novel Method for Profiling Side Channel Attacks against Imbalanced Data[J]. Journal of Cryptologic Research, 2021, 8(4): 549-559.
	汪平, 郑梦策, 南杰慧, 等. 一种针对侧信道建模攻击中数据不平衡的新方法[J]. 密码学报, 2021, 8(4): 549-559.
[11]	DUAN Xiaoyi, LI You, LINGHU Yunxing, et al. Research on the Method of Side Channel Attack Based on RF Algorithm[J]. Netinfo Security, 2022, 22(1): 19-26.
	段晓毅, 李邮, 令狐韫行, 等. 基于RF算法的侧信道攻击方法研究[J]. 信息网络安全, 2022, 22(1): 19-26.
[12]	CAO Jiahua, WU Zhen, WANG Yi, et al. Side Channel Attack of S-Box Power Randomization Based on CNN-BPR[J]. Journal of Chengdu University of Information Technology, 2022, 37(1): 16-20.
	曹家华, 吴震, 王燚, 等. 基于CNN-BPR的S-Box功耗随机化侧信道攻击[J]. 成都信息工程大学学报, 2022, 37(1): 16-20.
[13]	ZHANG Guanghua, YAN Fengru, ZHANG Dongwen, et al. Insider Threat Detection Model Based on LSTM-Attention[J]. Netinfo Security, 2022, 22(2): 1-10.
	张光华, 闫风如, 张冬雯, 等. 基于LSTM-Attention的内部威胁检测模型[J]. 信息网络安全, 2022, 22(2): 1-10.
[14]	XU Guotian, SHENG Zhenwei. DGA Malicious Domain Name Detection Method Based on Fusion of CNN and LSTM[J]. Netinfo Security, 2021, 21(10): 41-47.
	徐国天, 盛振威. 基于融合CNN与LSTM的DGA恶意域名检测方法[J]. 信息网络安全, 2021, 21(10): 41-47.
[15]	ZHOU Guobing, WU Jianxin, ZHANG Chenlin, et al. Minimal Gated Unit for Recurrent Neural Networks[J]. International Journal of Automation & Computing, 2016(13): 226-234.
[16]	STANDAERT F X, MALKIN T G, YUNG M. A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks[C]//Springer. 28th Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT 2009), Heidelberg: Springer, 2009: 443-461.
[17]	BENADJILA R, PROUFF E, STRULLU R, et al. Deep Learning for Side-Channel Analysis and Introduction to ASCAD Database[J]. Journal of Cryptographic Engineering, 2020(10): 163-188.
[18]	WANG Huanyu, BRISFORS M, FORSMARK S, et al. How Diversity Affects Deep-Learning Side-Channel Attacks[C]//IEEE. 2019 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC). New York: IEEE, 2019:1-16.

数据集	训练样本数量/个	测试样本数量/个	最大抖动
ASCAD	50000	10000	0
ASCAD_desync50	50000	10000	50
ASCAD_desync100	50000	10000	100

网络类型	参数设置	激活函数
输入层	尺寸为700•1	—
卷积层	卷积核为70•1,个数为4	ReLU
BN层	—	—
池化层	均值池化,步长为5,尺寸为5	—
全连接层	—	—
MGU层	神经元数为256	ReLU
Dropout层	Drop_out值为0.5	—
输出层	神经元数为256	Softmax

模型名称	网络层数	参数量/个
LSTM	3	1241856
CNN	13	26806656
CNN-MGU	7	442924

模型名称	训练时间	训练准确率
LSTM	1h:11m:35s	90.0%
CNN	5h:36m:21s	90.0%
CNN-MGU	34m:16s	90.0%