TY - GEN
T1 - Why Always Distance
T2 - 2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023
AU - Zhang, Yuwei
AU - Wang, An
AU - Guo, Hongchen
AU - Ding, Yaoling
AU - Sun, Shaofei
AU - Chen, Jiazhe
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Side-Channel analysis poses a significant threat to the security of cryptographic devices. Distance-based clustering methods include K-means and its variants are popularly applied on side-channel analysis in public key cryptography. However, distance-based clustering perform poorly in irregular data distributions. In this paper, we combine density-based clustering methods, such as DBSCAN and OPTICS, with side-channel analysis. In comparison to distance-based clustering methods, density-based methods are better suited for handling irregular data distributions and demonstrate greater robustness against noise. With density-based clustering methods, We successfully recover the operations on power traces with accuracy of 100% on both ECC-Card and ECC-FPGA, while the max accuracy of distance-based clustering methods is only 68.66% on the two datasets.
AB - Side-Channel analysis poses a significant threat to the security of cryptographic devices. Distance-based clustering methods include K-means and its variants are popularly applied on side-channel analysis in public key cryptography. However, distance-based clustering perform poorly in irregular data distributions. In this paper, we combine density-based clustering methods, such as DBSCAN and OPTICS, with side-channel analysis. In comparison to distance-based clustering methods, density-based methods are better suited for handling irregular data distributions and demonstrate greater robustness against noise. With density-based clustering methods, We successfully recover the operations on power traces with accuracy of 100% on both ECC-Card and ECC-FPGA, while the max accuracy of distance-based clustering methods is only 68.66% on the two datasets.
KW - ECC
KW - dense-based clustering
KW - side-channel analysis
UR - http://www.scopus.com/inward/record.url?scp=85184992830&partnerID=8YFLogxK
U2 - 10.1109/ICICN59530.2023.10392589
DO - 10.1109/ICICN59530.2023.10392589
M3 - Conference contribution
AN - SCOPUS:85184992830
T3 - ICICN 2023 - 2023 IEEE 11th International Conference on Information, Communication and Networks
SP - 405
EP - 410
BT - ICICN 2023 - 2023 IEEE 11th International Conference on Information, Communication and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 August 2023 through 20 August 2023
ER -