Generic side-channel attacks, unlike traditional CPA/DPA which are specialized to individual cryptographic circuits, can take in an arbitrary circuit or its power model and try to learn user-designated secrets from its side-channel traces. In this paper, we explore the use of machine learning in the context of such generic attacks. We discuss and demonstrate the challenges of using end-to-end (trace-to-key) learning on generic circuits with larger key sizes. We instead propose a couple of ways to use machine learning to assist recent pseudo-Boolean solver-based generic attacks and report their effectiveness on FPGA power traces.
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Enhancing Solver-based Generic Side-Channel Analysis with Machine Learning
Semantic Scholar · Computer Science · 2023
Abstract
Generic side-channel attacks, unlike traditional CPA/DPA which are specialized to individual cryptographic circuits, can take in an arbitrary circuit or its power model and try to learn user-designated secrets from its side-channel traces. In this paper, we explore the use of machine learning in the context of such generic attacks. We discuss and demonstrate the challenges of using end-to-end (trace-to-key) learning on generic circuits with larger key sizes. We instead propose a couple of ways to use machine learning to assist recent pseudo-Boolean solver-based generic attacks and report their effectiveness on FPGA power traces.