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The project takes the rapidly evolving advances in deep learning and applies them in the context of side-channel analysis (SCA). Finding SCA leakages on real devices can be a tedious process, resulting devices ranging from wearables to embedded Internet of Things (IoT) devices entering the marketplace without proper protection. This project explores ways to automate side-channel security analysis using deep learning techniques. To protect devices against SCA, the project also explores a novel approach to countermeasure design by applying the concept of adversarial learning.
SCA is essentially one complex statistical signal processing problem, which deep learning is ideally suited to solve. The project systematically quantifies the impact of deep learning on SCA by applying deep learning methods to all necessary steps in SCA, namely alignment, noise reduction, feature extraction and model building. Meaningful parameter sets for a representative list of reference targets are explored. The project also adapts adversarial learning techniques to counteract optimized side-channel information recovery, thereby inventing an entirely new class of side-channel countermeasures, where machine learning adaptively shapes leakage signals to prevent correct classification.
The SCA analysis and protection tools explored in this project will be invaluable for the health of our national computing and communications infrastructure. They will be released as an easy-to-use open-source toolbox. Furthermore, the project provides new insights and training for the next generation of experts at the intersection of two critical technologies, i.e. artificial intelligence and security.
More information on the project, including important data and developed code, is available at: http://v.wpi.edu/research/superhuman, until circa 2026.
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SaTC: CORE: Small: Super-Human Cryptanalysis for Scalable Side-Channel Analysis