ERI: Foundations of Machine Learning for Side-channel Analysis

针对现实世界中硬件平台受到的攻击，对其漏洞和相应对策的研究也变得更加重要。作为这种攻击的主要例子，可以提到针对用于认证的硬件设备中使用的用户私钥的攻击，其中分析电磁辐射、功率消耗和定时(通常被称为旁通道泄漏)导致危害设备的安全性。由于这种利用旁路攻击的有效性，人们对此给予了极大的关注，特别是人工智能辅助的安全验证。标准化机构(如国家标准和技术研究所)和认证机构参与与开发旁通道分析工具有关的活动，进一步突出了这一事项的重要性。尽管在这方面作出了努力，但由于已知的旁路攻击的数量不断增加，评估旁路的复原力可能不太实际。为了解决这一问题，并与标准化活动相一致，该项目旨在解决依赖机器学习的现有方法的一些根本缺点，特别是深度学习，将其作为评估设备免受旁路攻击的安全的强大工具。因此，通过该项目获得的成果预计将提高数百万台设备的安全性，这些设备用于医疗保健、情报、金融、交通和国防等敏感应用。此外，在该项目中开展的研究活动与教育和外联工作紧密结合，因为研究生和本科生都将参与该项目，从而获得硬件安全方面的尖端技能和专业知识。从技术上讲，本项目将回答以下关键问题：1)哪种深度学习模型能够以较低的复杂性和较高的可解释性近似于密码实现的泄漏特性？2)如何解释从一个实现实例到另一个实现实例所获得的结果的泛化？3)除了深度学习之外，还有哪些其他机器学习框架可以应用来保证密码系统对机器学习增强的侧通道分析是健壮的？在这样做的过程中，该项目创造了一套新的技术，依靠深度学习的理论基础，在实践中评估密码系统及其各自的嵌入式设备的安全性。此外，将在真实世界的密码实现上进行实验，即无保护和具有针对旁路分析的对策。这些系统的侧通道测量将公开提供。这确实是为了满足专注于密码系统安全评估的标准化和认证机构、行业和研究人员的需求。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。

In light of the attacks against real-world hardware platforms, the research on their vulnerabilities and corresponding countermeasures has gained further importance. As a prime example of such attacks, one targeting the users’ private keys used in hardware devices for authentication can be mentioned, where analyzing electromagnetic emanation, power consumption, and timing (often referred to as side-channel leakages) results in compromising the security of the device. Due to the effectiveness of such attacks leveraging side-channels, a great deal of attention has been paid to, particularly AI-assisted security verification. The involvement of standardization (e.g., National Institute of Standards and Technology) and certification bodies in the activities related to the development of tools for side-channel analysis further highlights the importance of this matter. In spite of the effort made in this respect, due to difficulties with regard to the constantly growing number of known side-channel attacks, evaluation of side-channel resiliency could be less practical. To tackle this and in line with the standardization activities, this project aims to address some of the fundamental shortcomings of the existing approaches relying on machine learning, specifically, deep learning, as a powerful tool to assess the security of devices against side-channel attacks. Hence, the results obtained through this project are expected to improve the security of millions of devices used across sensitive applications, including healthcare, intelligence, finance, transportation, and defense. Moreover, the research activities performed in this project are closely integrated with education and outreach efforts as both graduate, and undergraduate students will work on the project, thus gaining cutting-edge skills and expertise in hardware security. More technically, this project will answer these vital questions: 1) Which deep learning model can approximate the leakage properties of a cryptographic implementation with less complexity and high interpretability? 2) How can the generalization of the results achieved for one instance of the implementation to another be explained? 3) Besides deep learning, which other machine learning frameworks, preferably provable ones, can be applied to offer a guarantee that a cryptosystem is robust against machine learning-enhanced side-channel analysis? In doing so, the project creates a novel set of techniques relying on the theoretical foundations of deep learning to assess the security of cryptosystems and their respective embedded devices in practice. Furthermore, experimentation on real-world cryptographic implementations, namely unprotected and with countermeasures against side-channel analysis, will be conducted. Side-channel measurements from these systems will be made publicly available. This is indeed geared to the needs of the standardization and certification bodies, industry, and researchers focusing on security evaluation for cryptosystems.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.