Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147152
Title: On use of deep learning for side channel evaluation of black box hardware AES engine
Authors: Won, Yoo-Seung
Bhasin, Shivam
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Won, Y. & Bhasin, S. (2021). On use of deep learning for side channel evaluation of black box hardware AES engine. 7th EAI International Conference on Industrial Networks and Intelligent Systems (INISCOM 2021), 379, 185-194. https://dx.doi.org/10.1007/978-3-030-77424-0_15
Abstract: With the increasing demand for security and privacy, there has been an increasing availability of cryptographic acclerators out of the box in modern microcontrollers, These accelerators are optimised and often black box. Thus, proper evaluation against vulnerabilities like side-channel attacks is a challenge in absence of architecture information and thus leakage model. In this paper, we show the use of deep learning based side-channel attack can overcome this challenge, allowing evaluation of black box AES hardware engine on a secure microcontroller, without the knowledge of precise leakage model information. Our results report full key recovery with only 3,000 traces under a profiling setting.
URI: https://hdl.handle.net/10356/147152
ISBN: 978-3-030-77423-3
ISSN: 1867-8211
DOI: 10.1007/978-3-030-77424-0_15
Rights: © 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. All rights reserved. This paper was published by Springer Nature Switzerland AG in Proceedings of 7th EAI International Conference on Industrial Networks and Intelligent Systems (INISCOM 2021) and is made available with permission of ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:TL Conference Papers

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