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Title: Mind the portability : a warriors guide through realistic profiled side-channel analysis
Authors: Bhasin, Shivam
Chattopadhyay, Anupam
Heuser, Annelie
Jap, Dirmanto
Picek, Stjepan
Shrivastwa, Ritu Ranjan
Keywords: Science::Mathematics::Discrete mathematics::Cryptography
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2020
Source: Bhasin, S., Chattopadhyay, A., Heuser, A., Jap, D., Picek, S. & Shrivastwa, R. R. (2020). Mind the portability : a warriors guide through realistic profiled side-channel analysis. 27th Annual Network and Distributed System Security Symposium (NDSS 2020), 1-15.
Project: NRF2018–NCR–NCR009–0001 
Abstract: Profiled side-channel attacks represent a practical threat to digital devices, thereby having the potential to disrupt the foundation of e-commerce, the Internet of Things (IoT), and smart cities. In the profiled side-channel attack, the adversary gains knowledge about the target device by getting access to a cloned device. Though these two devices are different in real- world scenarios, yet, unfortunately, a large part of research works simplifies the setting by using only a single device for both profiling and attacking. There, the portability issue is conveniently ignored to ease the experimental procedure. In parallel to the above developments, machine learning techniques are used in recent literature, demonstrating excellent performance in profiled side-channel attacks. Again, unfortunately, the portability is neglected. In this paper, we consider realistic side-channel scenarios and commonly used machine learning techniques to evaluate the influence of portability on the efficacy of an attack. Our experimental results show that portability plays an important role and should not be disregarded as it contributes to a significant overestimate of the attack efficiency, which can easily be an order of magnitude size. After establishing the importance of portability, we propose a new model called the Multiple Device Model (MDM) that formally incorporates the device to device variation during a profiled side-channel attack. We show through experimental studies how machine learning and MDM significantly enhance the capacity for practical side-channel attacks. More precisely, we demonstrate how MDM can improve the performance of an attack by order of magnitude, completely negating the influence of portability.
ISBN: 1-891562-61-4
DOI: 10.14722/ndss.2020.24390
Rights: © 2020 The Author(s) (published by Internet Society). This is an open-access article distributed under the terms of Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers
TL Conference Papers

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