Cryptology ePrint Archive: Report 2019/661

Mind the Portability: A Warriors Guide through Realistic Profiled Side-channel Analysis

Shivam Bhasin and Anupam Chattopadhyay and Annelie Heuser and Dirmanto Jap and Stjepan Picek and Ritu Ranjan Shrivastwa

Abstract: Profiled side-channel attacks represent a practical threat to digital devices, thereby having the potential to disrupt the foundation of e-commerce, Internet-of-Things (IoT), and smart cities. In the profiled side-channel attack, 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 in order 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 enhances the capacity for practical side-channel attacks. More precisely, we demonstrate how MDM is able to improve the results by $>10\times$, completely negating the influence of portability.

Category / Keywords: Side-channel attacks, Machine learning, Portability, Overfitting, Multiple Device Model

Date: received 4 Jun 2019, last revised 12 Jun 2019

Contact author: picek stjepan at gmail com, annelie heuser@irisa fr, sbhasin@ntu edu sg, anupam@ntu edu sg, djap@ntu edu sg

Available format(s): PDF | BibTeX Citation

Version: 20190612:085010 (All versions of this report)

Short URL: ia.cr/2019/661


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