TY - GEN
T1 - Using Convolutional Neural Network to Redress Outliers in Clustering Based Side-Channel Analysis on Cryptosystem
AU - Wang, An
AU - He, Shulin
AU - Wei, Congming
AU - Sun, Shaofei
AU - Ding, Yaoling
AU - Wang, Jiayao
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Blockchain, designed with cryptographic technology, is widely used in the financial area, such as digital billing and cross-border payments. Digital signature is the core technology in it. However, digital signatures in public key cryptosystems face the threat of simple power analysis in Side-Channel Analysis (SCA). The state-of-the-art simple power analysis based on clustering mostly will appear outliers in the process of analysis, which will reduce success rate of key recover. In this paper, we propose a new SCA method with clustering algorithm Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and deep learning technology Convolutional Neural Network (CNN), called DBSCAN-CNN, to analyze public key cryptosystems. We cluster data with DBSCAN firstly. Then we train a CNN model based on the trusted clustering results. Finally, we classify the outliers of clustering results by the trained model. We mount the proposed method to analyze an FPGA-based elliptic curve scalar multiplication power trace which is desynchronized by simulating random delay. The experimental results show that the error rate of the proposed method is at least 69.23 % lower than that of the classical clustering method in SCA.
AB - Blockchain, designed with cryptographic technology, is widely used in the financial area, such as digital billing and cross-border payments. Digital signature is the core technology in it. However, digital signatures in public key cryptosystems face the threat of simple power analysis in Side-Channel Analysis (SCA). The state-of-the-art simple power analysis based on clustering mostly will appear outliers in the process of analysis, which will reduce success rate of key recover. In this paper, we propose a new SCA method with clustering algorithm Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and deep learning technology Convolutional Neural Network (CNN), called DBSCAN-CNN, to analyze public key cryptosystems. We cluster data with DBSCAN firstly. Then we train a CNN model based on the trusted clustering results. Finally, we classify the outliers of clustering results by the trained model. We mount the proposed method to analyze an FPGA-based elliptic curve scalar multiplication power trace which is desynchronized by simulating random delay. The experimental results show that the error rate of the proposed method is at least 69.23 % lower than that of the classical clustering method in SCA.
KW - Convolutional Neural Network
KW - DBSCAN
KW - Outlier detection
KW - Public-key cryptosystems
KW - Side-Channel Analysis
UR - http://www.scopus.com/inward/record.url?scp=85152577495&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-28124-2_34
DO - 10.1007/978-3-031-28124-2_34
M3 - Conference contribution
AN - SCOPUS:85152577495
SN - 9783031281235
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 360
EP - 370
BT - Smart Computing and Communication - 7th International Conference, SmartCom 2022, Proceedings
A2 - Qiu, Meikang
A2 - Lu, Zhihui
A2 - Zhang, Cheng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on Smart Computing and Communication, SmartCom 2022
Y2 - 18 November 2022 through 20 November 2022
ER -