Machine learning in side-channel analysis: a first study

Abstract

Electronic devices may undergo attacks going beyond traditional cryptanalysis. Side-channel analysis (SCA) is an alternative attack that exploits information leaking from physical implementations of e.g. cryptographic devices to discover cryptographic keys or other secrets. This work comprehensively investigates the application of a machine learning technique in SCA. The considered technique is a powerful kernel-based learning algorithm: the Least Squares Support Vector Machine (LS-SVM). The chosen side-channel is the power consumption and the target is a software implementation of the Advanced Encryption Standard. In this study, the LS-SVM technique is compared to Template Attacks. The results show that the choice of parameters of the machine learning technique strongly impacts the performance of the classification. In contrast, the number of power traces and time instants does not influence the results in the same proportion. This effect can be attributed to the usage of data sets with straightforward Hamming weight leakages in this first study.

Reliable Information Extraction for Single Trace Attacks

“…Recent work [8]- [10] investigated machine learning techniques, and in particular Support Vector Machines (SVM), as an alternative to classical template building approaches. SVM are probably the most prominent member of kernel methods.…”

Template Attacks vs. Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis)

“…The stochastic approach exploiting Linear Regression (LR) is a frequently considered alternative [22]. More recently, solutions relying on Machine Learning (ML) have also been investigated [2,11,13,12,16,17,19]. These previous works support the claim that ML-based attacks are effective and lead to successful key recoveries.…”

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

“…Hospodar et al [40], [41] analyzed the SVM on a software implementation of a block cipher. Heuser et al [42] 1Typical for smart cards.…”

“…In previous works, individual machine learning (ML) approaches are compared mostly with the template attack or the stochastic attack (SA) [40], [41]. ML approaches have not been compared yet.…”