Collaborative Research: SaTC: CORE: Small: Securing Brain-inspired Hyperdimensional Computing against Design-time and Run-time Attacks for Edge Devices

许多计算应用程序依赖于机器学习(ML)算法，这些算法分析数据中的模式并对他们遇到的新数据进行预测。这些机器学习分类器中的许多最新进展使用基于神经网络的方法；然而，神经网络通常需要大量的数据、内存和处理能力。近年来，大脑启发的超维计算(HDC)作为一种不那么耗费资源的方法出现，用于构建非常适合于计算能力较弱的较小计算设备的分类器。然而，就像其他ML分类器架构一样，HDC模型可能会受到攻击者的威胁，这些攻击者想要降低模型的性能，插入后门“触发器”，让攻击者通过提供秘密输入来控制设备，或者窃取模型本身。然而，HDC模型中的这些安全风险并没有像HDC性能那样得到充分的研究。该项目的目标是通过更好地了解HDC安全漏洞和防御措施来缩小这一差距。这包括分析HDC模型可能受到攻击的空间，将神经网络中的攻击和防御与HDC模型中的攻击和防御进行比较，并开发与HDC模型本身一样有效、高效和轻量级的防御措施，以便它们也可以部署在计算能力有限的设备上。该项目通过系统地研究HDC计算从设计时到运行时和从算法到硬件的攻击面，为基于HDC的边缘设备推理铺平了道路。首先，探讨了与HDC相关的漏洞，并系统地定义了其独特的攻击面。因此，它调查了来自敌意输入、模型扰动和逆向工程对HDC模型性能和隐私的关键威胁。其次，它通过结合算法、硬件和系统级别的方法来探索有效和高效的防御策略。拟议工作中的一个关键见解和工具是将基于神经网络的模型和HDC模型相关联的方法；这将允许进行比较研究，并为使基于神经网络的体系结构的现有攻击和防御适应HDC环境提供了可能。科学成果将有助于重塑支持HDC的计算系统，使其具有更高的安全性和健壮性。该项目还包含了重要的教育内容，并提供了大量的机会来培养和吸引来自代表性不足群体的学生从事计算机科学和计算机科学研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many computing applications depend on machine learning (ML) algorithms that analyze patterns in data and make predictions about new data they encounter. Many recent advances in these machine learning classifiers use approaches based on neural networks; however, neural networks often require large amounts of data, memory, and processing power. Brain-inspired hyperdimensional computing (HDC) has emerged in recent years as a less resource-heavy approach to building classifiers that are well-suited for smaller computing devices that have less computing power. However, just like other ML classifier architectures, HDC models may be threatened by attackers who want to degrade the models' performance, insert backdoor "triggers" that let attackers take control of devices by presenting secret inputs, or steal the models themselves. However, these security risks in HDC models are not as well-studied as HDC performance. This project's goal is to close that gap through a better understanding of HDC security vulnerabilities and defenses. This includes analyzing the space of possible attacks on HDC models, drawing parallels between attacks and defenses in neural networks and those in HDC models, and developing defenses that are as effective, efficient, and lightweight as the HDC models themselves so they can too be deployed in devices with limited computing power.This project paves the way for HDC-based inference on edge devices by systematically investigating the attack surface for HDC computing, from design time to run time and from algorithm to hardware. First, it explores the vulnerabilities associated with HDC and systematically defines its unique attack surface. Accordingly, it investigates critical threats against HDC model performance and privacy from adversarial input, model perturbation, and reverse engineering. Second, it explores effective and efficient defense strategies by incorporating algorithmic-, hardware-, and system-level methods. A key insight and tool in the proposed work are methods for relating neural network-based models and HDC models; this will allow for comparative studies as well as open possibilities for adapting existing attacks and defenses on neural network-based architectures to HDC contexts. The scientific outcomes will help reshape HDC-enabled computing systems toward greater security and robustness. The project also contains a significant educational component and provides abundant opportunities to nurture and attract students from under-represented groups to engage in computer science and computer science research.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.