An Automated End-to-End Side Channel Analysis Based on Probabilistic Model

Jeonghwan Hwang,Ji Won Yoon

doi:10.3390/app10072369

Abstract

In this paper, we propose a new automated way to find out the secret exponent from a single power trace. We segment the power trace into subsignals that are directly related to recovery of the secret exponent. The proposed approach does not need the reference window to slide, templates nor correlation coefficients compared to previous manners. Our method detects change points in the power trace to explore the locations of the operations and is robust to unexpected noise addition. We first model the change point detection problem to catch the subsignals irrelevant to the secret and solve this problem with Markov Chain Monte Carlo (MCMC) which gives a global optimal solution. After separating the relevant and irrelevant parts in signal, we extract features from the segments and group segments into clusters to find the key exponent. Using single power trace indicates the weakest power level of attacker where there is a very slight chance of acquiring as many power traces as needed for breaking the key. We empirically show the improvement in accuracy even with presence of high level of noise.

Highlights

Many Side Channel Analysis attacks have succeeded in breaking the secret keys analyzing power trace(s) generated from devices
We suggest the methods to compute the probability of locations from which the secret came and find out global optimal solutions in a Monte Carlo approach
Though σ and V should be optimized for the best inference, we have experimentally chosen the values of σ and V among the candidates that were sampled during Markov Chain Monte Carlo (MCMC) process

Summary

Introduction

Many Side Channel Analysis attacks have succeeded in breaking the secret keys analyzing power trace(s) generated from devices. There are many assumptions and limitations on these attacks. Some of them exploit as many power traces as needed to recover secrets. Others have proposed methods to recover secrets from overall power trace(s) but not their exact location on the power trace from which each bit of secrets have been recovered. One of the well known approaches is to find the reference window and apply a peak detecting algorithm [1,2]. The success of this approach heavily depends on the selection of a “good”

Objectives

Results

Discussion

Conclusion