Semi-Supervised Template Attack

Lerman, Liran; Medeiros, Stephane Fernandes; Veshchikov, Nikita; Meuter, Cédric; Bontempi, Gianluca; Markowitch, Olivier

doi:10.1007/978-3-642-40026-1_12

Cited by 26 publications

(10 citation statements)

References 19 publications

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“…In [49], this MLP approach was optimized by using the preprocessing of the power traces measured. Lerman et al [50] introduced semisupervised a Template Attack, that combines supervised and unsupervised learning. The method was confirmed by the experiments on an 8-bit microcontroller and by a comparison to a template attack.…”

Section: Related Workmentioning

confidence: 99%

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Malina¹,

Zeman²,

Martinasek³

et al. 2016

RADIOENGINEERING

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Section: Related Workmentioning

confidence: 99%

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Malina¹,

Zeman²,

Martinasek³

et al. 2016

RADIOENGINEERING

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“…For example, in [10] a non-profiling attack was first used to recover a key prior to subsequently using the broken device to build templates. In [20], it was shown how a device with a faulty random number generator (RNG) suffices to build templates, while in [21] it was shown how two devices with different unknown keys could be used. It was also suggested in [22] that public verification functions could be used to build templates using the device under attack itself.…”

Section: Profiling Attacksmentioning

confidence: 99%

Empirical evaluation of multi-device profiling side-channel attacks

Hanley¹,

O’Neill²,

Tunstall

et al. 2014

2014 IEEE Workshop on Signal Processing Systems (SiPS)

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Side-channel analysis of cryptographic systems can allow for the recovery of secret information by an adversary even where the underlying algorithms have been shown to be provably secure. This is achieved by exploiting the unintentional leakages inherent in the underlying implementation of the algorithm in software or hardware. Within this field of research, a class of attacks known as profiling attacks, or more specifically as used here template attacks, have been shown to be extremely efficient at extracting secret keys. Template attacks assume a strong adversarial model, in that an attacker has an identical device with which to profile the power consumption of various operations. This can then be used to efficiently attack the target device. Inherent in this assumption is that the power consumption across the devices under test is somewhat similar. This central tenet of the attack is largely unexplored in the literature with the research community generally performing the profiling stage on the same device as being attacked. This is beneficial for evaluation or penetration testing as it is essentially the best case scenario for an attacker where the model built during the profiling stage matches exactly that of the target device, however it is not necessarily a reflection on how the attack will work in reality. In this work, a large scale evaluation of this assumption is performed, comparing the key recovery performance across 20 identical smart-cards when performing a profiling attack.

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“…To the best of our knowledge, the only work up till now implementing a semisupervised analysis in SCA is [10], where the authors conclude that the semisupervised setting cannot compete with a supervised setting. Unfortunately, the assumed scenario is hard to justify and consequently their results are expected (but without much implication for SCA).…”

Section: Introductionmentioning

confidence: 97%

Improving Side-Channel Analysis Through Semi-supervised Learning

Picek

Heuser

Jović

et al. 2019

Smart Card Research and Advanced Applications

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The profiled side-channel analysis represents the most powerful category of side-channel attacks. In this context, the security evaluator (i.e., attacker) gains access to a profiling device to build a precise model which is used to attack another device in the attacking phase. Mostly, it is assumed that the attacker has significant capabilities in the profiling phase, whereas the attacking phase is very restricted. We step away from this assumption and consider an attacker restricted in the profiling phase, while the attacking phase is less limited. We propose the concept of semi-supervised learning for side-channel analysis, where the attacker uses a small number of labeled measurements from the profiling phase as well as the unlabeled measurements from the attacking phase to build a more reliable model. Our results show that the semisupervised concept significantly helps the template attack (TA) and its pooled version (TAp). More specifically, for low noise scenario, the results for machine learning techniques and TA are often improved when only a small number of measurements is available in the profiling phase, while there is no significant difference in scenarios where the supervised set is large enough for reliable classification. For high noise scenario, TAp and multilayer perceptron results are improved for the majority of inspected dataset sizes, while for high noise scenario with added countermeasures, we show a small improvement for TAp, Naive Bayes and multilayer perceptron approaches for most inspected dataset sizes. Current results go in favor of using semi-supervised learning, especially self-training approach, in side-channel attacks.

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Semi-Supervised Template Attack

Cited by 26 publications

References 19 publications

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Empirical evaluation of multi-device profiling side-channel attacks

Improving Side-Channel Analysis Through Semi-supervised Learning

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