SaTC: CORE: Small: Generic Circuit Learning from Adaptive Side-Channel Queries

该项目旨在开发算法和技术，使人们能够以通用（适用于任意电路）和自适应（算法与电路智能交互）的方式，从电路的功率、时序和电磁辐射测量（所谓的侧信道）中了解数字或模拟电路的隐藏结构。这些算法可以用于各种应用程序，最重要的是作为硬件安全性和完整性检查库中的工具。鉴于网络威胁的增加，特别是那些涉及硬件访问的多用途自适应工具变得越来越有价值。该项目旨在以（开源）软件、基准测试、论文、机器学习数据集和培训未来专业劳动力的形式提供开发的技术。该项目通过三个技术任务来探索这个问题：第一个任务致力于数字电路，重点是基于求解器、基于电路和基于统计/机器学习的解决方案。第二项任务是专门研究模拟和混合信号电路。这包括探索如何对任意模拟电路侧通道建模，并在学习过程中使用非线性求解器和优化器与它们交互。第三个任务致力于利用、评估和演示。这包括从前两个任务中获取结果，并将其用于各种电路保护方案的安全分析，以防止侧信道攻击，并检测恶意硬件修改。软件仿真，可编程硬件，分立元件，加上两个芯片设计被用来测试所提出的方法和算法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to develop algorithms and techniques for allowing one to learn the hidden structure of a digital or analog circuit from power, timing, and electro-magnetic radiation measurements (so-called side-channels) of the circuit, in a generic (applicable to arbitrary circuits) and adaptive (the algorithm intelligently interacts with the circuit) manner. Such algorithms can be used in various applications most importantly as tools in the arsenal of hardware security and integrity checking. Given the rise in cyber threats especially those that involve hardware access to such versatile adaptive tools become increasingly valuable. The project aims to deliver the developed technology in the form of (open-source) software, benchmarks, papers, machine-learning datasets, and training of an expert future workforce.The project explores this problem along three technical tasks: The first task is dedicated to digital circuits with a focus on solver-based, circuit-based, and statistical/machine-learning-based solutions. The second task is dedicated to analog and mixed-signal circuits. This involves exploring how to model arbitrary analog circuit side-channels and having non-linear solvers and optimizers interact with them for the learning process. The third task is dedicated to utilization, evaluation, and demonstration. This involves taking results from the first two tasks and using them in the context of security analysis of various circuit protection schemes against side-channel attacks, and the detection of malicious hardware modifications. Simulation in software, programmable hardware, discrete components, plus two chip designs are being used to test the proposed methods and algorithms as well.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.