Abstract
In this work, we ask a question whether Convolutional Neural Networks are more suitable for side-channel attacks than some other machine learning techniques and if yes, in what situations. Our results point that Convolutional Neural Networks indeed outperform machine learning in several scenarios when considering accuracy. Still, often there is no compelling reason to use such a complex technique. In fact, if comparing techniques without extra steps like preprocessing, we see an obvious advantage for Convolutional Neural Networks when the level of noise is small, and the number of measurements and features is high. The other tested settings show that simpler machine learning techniques, for a significantly lower computational cost, perform similarly or sometimes even better. The experiments with guessing entropy indicate that methods like Random Forest or XGBoost could perform better than Convolutional Neural Networks for the datasets we investigated.
Original language | English |
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Title of host publication | Security, Privacy, and Applied Cryptography Engineering |
Subtitle of host publication | 8th International Conference, SPACE 2018, Kanpur, India, December 15-19, 2018, Proceedings |
Editors | A. Chattopadhyay, C. Rebeiro, Y. Yarom |
Place of Publication | Cham |
Publisher | Springer |
Pages | 157-176 |
Number of pages | 20 |
ISBN (Electronic) | 978-3-030-05072-6 |
ISBN (Print) | 978-3-030-05071-9 |
DOIs | |
Publication status | Published - 2018 |
Event | SPACE 2018: International Conference on Security, Privacy, and Applied Cryptography Engineering : 8th International Conference - Kanpur, India Duration: 15 Dec 2018 → 19 Dec 2018 Conference number: 8th |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 11348 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | SPACE 2018: International Conference on Security, Privacy, and Applied Cryptography Engineering |
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Country/Territory | India |
City | Kanpur |
Period | 15/12/18 → 19/12/18 |
Keywords
- Side-channel analysis
- Machine learning
- Deep learning
- Convolutional Neural Networks