Naslov
Side-Channel Analysis and Machine Learning: A Practical Perspective

Autori
Picek, Stjepan ; Heuser, Annelie ; Jović, Alan ; Ludwig, Simone A. ; Guilley, Sylvain ; Jakobović, Domagoj ; Mentens, Nele

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the International Joint Conference on Neural Networks / - Anchorage (AK) : The Printing House, Inc, IEEE, 2017, 4095-4102

ISBN
978-1-5090-6181-5

Skup
IEEE International Joint Conference on Neural Networks (IJCNN)

Mjesto i datum
Anchorage (AK), Sjedinjene Američke Države, 14.05.2017. - 19.05.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
side-channel analysis ; machine learning techniques ; profiling ; parameter tuning ; data mining

Sažetak
The field of side-channel analysis has made significant progress over time. Side-channel analysis is now used in practice in design companies as well as in test laboratories, and the security of products against side-channel attacks has significantly improved. However, there are still some remaining issues to be solved for side-channel analysis to become more effective. Side-channel analysis consists of two steps, commonly referred to as identification and exploitation. The identification consists of understanding the leakage and building suitable models. The exploitation consists of using the identified leakage models to extract the secret key. In scenarios where the model is poorly known, it can be approximated in a profiling phase. There, machine learning techniques are gaining value. In this paper, we conduct extensive analysis of several machine learning techniques, showing the importance of proper parameter tuning and training. In contrast to what is perceived as common knowledge in unrestricted scenarios, we show that some machine learning techniques can significantly outperform template attacks when properly used. We therefore stress that the traditional worst case security assessment of cryptographic implementations, that mainly includes template attacks, might not be accurate enough. Besides that, we present a new measure called the Data Confusion Factor that can be used to assess how well machine learning techniques will perform on a certain dataset.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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