Excellence in Research: A Hierarchical Machine Learning Approach for Securing of NoC-Based MPSoCs Against Thermal Attacks

多处理器片上系统（MPSoC）的设计通常涉及集成预先设计的知识产权（IP）组件，以最大限度地降低成本并加快上市时间。这种方法为将被称为硬件木马（HT）的恶意电路插入到最终产品中的对手对制造过程的潜在操纵留下了空间。根据攻击者的意图，HT可以执行各种恶意任务，包括损害可靠性、导致操作失败、泄露信息和发起拒绝服务。该项目旨在解决与嵌入MPSoC中的HT感染热传感器相关的安全问题。鉴于热信息主要用于动态功率和热管理，因此监控MPSoC内热传感器的行为以检测和隔离受损传感器至关重要。该项目旨在通过采用分层机器学习（ML）方法来实现这一目标。为了监控MPSoC中热传感器的功能，从芯片内核获得的热信息将通过一系列小型到复杂的机器学习（ML）分类器进行处理。在最低级别，在目标MPSoC内的片上网络（NoC）路由器处实现的对策尝试识别受损的热传感器。然后，每个路由器收集的热数据被传输到芯片级ML分类器，该分类器用作专用ML加速器，能够捕获路由器级对策不易检测到的情况。随后，热数据通常被传输到云服务器以进行进一步的ML处理，用作更新片上ML分类器的权重的反馈机制。由于片上分类器的准确性通过学习来自基于云的分类器的反馈而提高，因此所提出的方法有可能解决具有不同概率特征和配置文件的攻击。该奖项反映了NSF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而被认为值得支持。

The design of Multi-Processor System-on-Chips (MPSoCs) often involves the integration of pre-designed Intellectual Property (IP) components to minimize costs and accelerate time to market. This approach leaves room for potential manipulation of the manufacturing process by adversaries who insert malicious circuitries known as Hardware Trojans (HTs) into the final product. Depending on the intentions of the adversary, an HT can perform various malicious tasks, including compromising reliability, causing operational failures, leaking information, and initiating denial of services. This project aims to address security concerns related to HT-infected thermal sensors embedded in MPSoCs. Given that thermal information is notably used in dynamic power and thermal management, it is crucial to monitor the behavior of thermal sensors within an MPSoC to detect and isolate compromised ones. This project aims to achieve this goal by employing a hierarchical machine learning (ML) approach. This project impacts a broad range of computing systems that utilize any of the commercially available MPSoCs on the market.In order to monitor the functionality of thermal sensors in an MPSoC, the thermal information obtained from the cores on the chip undergoes processing through a hierarchy of small to complex machine learning (ML) classifiers. At the lowest level, countermeasures implemented at the Network-on-Chip (NoC) routers within the target MPSoC try to identify compromised thermal sensors. The thermal data collected by each router is then transmitted to a chip-wide ML classifier, which functions as a dedicated ML accelerator, capable of capturing cases that are not easily detected by the router-level countermeasures. Subsequently, the thermal data is often transmitted to a cloud server for further ML processing, serving as a feedback mechanism to update the weights of the on-chip ML classifier. As the accuracy of the on-chip classifier improves through learning feedback from the cloud-based classifier, the proposed approach has the potential to address attacks with diverse probabilistic characteristics and profiles.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.