Davide Galli
DEIB, Politecnico di Milano, Milan, Italy

Giuseppe Chiari
DEIB, Politecnico di Milano, Milan, Italy

Davide Zoni
DEIB, Politecnico di Milano, Milan, Italy

Keywords: Side-Channel Analysis, AES, Hiding Techniques, Dataset, Deep Learning, RISC-V

Abstract

Side-channel attacks exploit unintended information leakage emitted by cryptographic devices to extract sensitive data. Hiding techniques are a cost-effective countermeasure designed to obfuscate the side-channel leakage and hinder these attacks. Available open datasets rely on artificial models to simulate hiding effects, preventing a realistic assessment of these countermeasures and, thus, leaving a pressing need for datasets offering real-world, obfuscated side-channel measurements. Chameleon introduces the first comprehensive dataset of real-world, obfuscated power traces collected from a RISC-V-based System-on-Chip. The traces are obfuscated using four state-of-the-art hiding techniques: dynamic frequency scaling, random delay, morphing, and chaffing. Chameleon captures real leakage deformations introduced by actual hardware implementations, making it a realistic and valuable tool for evaluating side-channel countermeasures. A key feature of Chameleon is its dual focus on the segmentation and attack stages of the side-channel analysis process. It is the first dataset designed to facilitate the challenging task of segmenting cryptographic operations from obfuscated traces, offering precise metadata that pinpoints the start and end of each operation. The high-quality metadata enables systematic research into segmentation techniques, a critical step often overlooked in previous datasets. Chameleon provides an essential platform for researchers to develop and test new side-channel attacks, highlighting the vulnerabilities of current hiding techniques. By offering a more realistic assessment of countermeasure effectiveness, Chameleon is an invaluable tool for advancing the state-of-the-art in the side-channel evaluation.