SaTC: CORE: Small: Super-Human Cryptanalysis for Scalable Side-Channel Analysis

该项目采用了深度学习中快速发展的进步，并将其应用于侧通道分析（SCA）的背景下。在真实的设备上查找SCA泄漏可能是一个繁琐的过程，导致从可穿戴设备到嵌入式物联网（IoT）设备的设备在没有适当保护的情况下进入市场。该项目探索了使用深度学习技术自动化侧通道安全分析的方法。为了保护设备免受SCA的影响，该项目还通过应用对抗学习的概念探索了一种新的对策设计方法。SCA本质上是一个复杂的统计信号处理问题，深度学习非常适合解决这个问题。该项目通过将深度学习方法应用于SCA中的所有必要步骤，即对齐、降噪、特征提取和模型构建，系统地量化了深度学习对SCA的影响。有意义的参数集的参考目标的代表性列表进行了探讨。该项目还采用对抗性学习技术来抵消优化的侧信道信息恢复，从而发明了一种全新的侧信道对策，其中机器学习自适应地塑造泄漏信号以防止正确分类。在这个项目中探索的SCA分析和保护工具对于我们国家计算和通信基础设施的健康将是非常宝贵的。它们将作为一个易于使用的开源工具箱发布。此外，该项目还为下一代专家提供了新的见解和培训，这些专家涉及两项关键技术，即人工智能和安全。有关该项目的更多信息，包括重要数据和开发的代码，可在http：//v.wpi.edu/research/superhuman上获得，直到大约2026年。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.