Why Always Distance: Dense-Based Clustering in Side-Channel Analysis

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 $\displaystyle \max$ accuracy of distance-based clustering methods is only 68.66% on the two datasets.

I. Introduction

Public key cryptography provide security for data transmission in communications and networks. Among them, Elliptic Curve Cryptography (ECC) is popular due to its high security, shorter key lengths, and lower computational complexity. SideChannel analysis (SCA) poses security threats to cryptographic algorithms. SCA was first proposed in 1996, which exploited the differences in execution times between the modular squaring and modular multiplication operations in RSA [1]. In addition to time, there are also other types of side-channels, such as sound [2], electromagnetic emission [3] [4], power [5] [6] and more.