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

卓越的研究：用于保护基于 NoC 的 MPSoC 免受热攻击的分层机器学习方法

• 批准号: 2302537
• 负责人: Ahmad Patooghy
• 金额: $ 57.6万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302537&HistoricalAwards=false
• 关键词: Excellence; Research; Hierarchical; Machine; Learning

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.

多处理器片上系统（mpsoc）的设计通常涉及预先设计的知识产权（IP）组件的集成，以最大限度地降低成本并加快上市时间。这种方法为对手对制造过程的潜在操纵留下了空间，他们将恶意电路（称为硬件木马（Hardware trojan, ht））插入最终产品中。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mpsoc中嵌入的受高温病毒感染的热传感器相关的安全问题。鉴于热信息主要用于动态电源和热管理，因此监测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）方法实现这一目标。该项目影响了使用市场上任何商用mpsoc的广泛计算系统。为了监测MPSoC中热传感器的功能，从芯片上的核心获得的热信息通过从小到复杂的机器学习（ML）分类器的层次结构进行处理。在最低级别，在目标MPSoC内的片上网络（NoC）路由器上实施的对策试图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范围内的机器学习分类器，该分类器作为专用的机器学习加速器，能够捕获不易被路由器级对策检测到的情况。随后，热数据通常被传输到云服务器进行进一步的机器学习处理，作为反馈机制来更新片上机器学习分类器的权重。由于片上分类器的准确性通过学习基于云的分类器的反馈而提高，因此所提出的方法有可能处理具有不同概率特征和特征的攻击。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。