Collaborative Research: SaTC: CORE: Medium: Cyber-threat Detection and Diagnosis in Multistage Manufacturing Systems through Cyber and Physical Data Analytics

协作研究：SaTC：核心：中：通过网络和物理数据分析进行多级制造系统中的网络威胁检测和诊断

• 批准号: 2019311
• 负责人: WenZhan Song
• 金额: $ 60万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019311&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Medium

In modern multistage manufacturing systems, with increased software-defined automation and control as well as monitoring of manufacturing assets across networks, exposure to cyber-attacks also grows. The cyber-threats may compromise the integrity of manufacturing assets (manufacturing systems and processes, machine tools, fabricated parts), reduce manufacturing productivity, and increase costs. Some cyber-threats including integrity attacks are only partially observable in cyberspace alone, and therefore need to be detected and diagnosed through inter-dependency analysis of both cyber and physical signals. Thus, there is a significant opportunity in exploring physical signals, together with cyber signals, to advance trustworthy manufacturing system research and design. This project brings together leading researchers from manufacturing systems, computer security, and electrical drives to develop and demonstrate a new methodology and tool for cyber-threat detection and diagnosis in multistage manufacturing systems. The cyber-security tool will monitor a variety of cyber and physical signals and perform cyber-threat detection and root cause diagnosis through advanced cyber-physical data fusion and taint analysis. The goal is to enable the prevention and mitigation of potential harms at the early stage, proactive and predictive maintenance, and countermeasures. This project attempts to integrate and analyze the process and quality signals, and the signals from the power networks and cyber networks of multistage manufacturing systems to detect and diagnose cyber-threats. This new systematic approach expects to capture new cyber-threats, especially data integrity attacks, that traditional cyber-security approaches may not capture. The proposed data analytics and methodology for integrating cyber and physical signals will advance a fundamental understanding of cyber-threat detection and diagnosis in multistage manufacturing systems and can broadly apply to other cyber-physical systems.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.

在现代多阶段制造系统中，随着软件定义的自动化和控制以及跨网络的制造资产监控的增加，网络攻击的风险也在增加。网络威胁可能会损害制造资产（制造系统和流程、机床、装配部件）的完整性，降低制造生产率并增加成本。包括完整性攻击在内的一些网络威胁仅在网络空间中部分可见，因此需要通过对网络和物理信号的相互依赖性分析来检测和诊断。因此，有一个重要的机会，在探索物理信号，与网络信号，以推进值得信赖的制造系统的研究和设计。该项目汇集了来自制造系统，计算机安全和电气驱动的领先研究人员，以开发和展示一种用于多阶段制造系统中网络威胁检测和诊断的新方法和工具。网络安全工具将监测各种网络和物理信号，并通过先进的网络物理数据融合和污染分析进行网络威胁检测和根本原因诊断。目标是在早期阶段预防和减轻潜在危害，主动和预测性维护以及对策。 该项目试图整合和分析过程和质量信号，以及来自多级制造系统的电力网络和网络网络的信号，以检测和诊断网络威胁。这种新的系统化方法有望捕获传统网络安全方法可能无法捕获的新网络威胁，特别是数据完整性攻击。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Unsupervised Anomaly Detection and Diagnosis in Power Electronic Networks: Informative Leverage and Multivariate Functional Clustering Approaches

电力电子网络中的无监督异常检测和诊断：信息杠杆和多元功能聚类方法

• 发表时间: 2023
• 期刊: IEEE transactions on smart grid
• 影响因子: 9.6
• 作者: Wu, Shushan;Fang, Luyang;JZhang, Jinan;Sriram, T.N.;Coshatt, Stephen;Zahiri, Feraidoon;Mantooth, Alan;Ye, Jin;Zhong, Wenxuan;Ma, Ping
• 通讯作者: Ma, Ping

A Four-layer Cyber-physical Security Model for Electric Machine Drives considering Control Information Flow

考虑控制信息流的电机驱动四层信息物理安全模型

• DOI: 10.1109/jestpe.2024.3366089
• 发表时间: 2024
• 期刊: IEEE Journal of Emerging and Selected Topics in Power Electronics
• 影响因子: 5.5
• 作者: Yang, Bowen;Yang, He;Ye, Jin
• 通讯作者: Ye, Jin

Design of Cyber-Physical Security Testbed for Multi-Stage Manufacturing System

多级制造系统信息物理安全测试台设计

• 发表时间: 2022
• 期刊: IEEE Global Communications Conference
• 作者: J. Coshatt, Stephen;Li, Qi;Yang, Bowen;Wu, Shuhan;Zahiri, Feraidoon;Shrivastava, Darpan;Ye, Jin;Song, WenZhan
• 通讯作者: Song, WenZhan

Vulnerability Assessments of Induction Machine-Based Multistage Rolling Mill System Under Sensor Integrity Attacks

• DOI: 10.1109/tii.2024.3370240
• 发表时间: 2024-06
• 期刊: IEEE Transactions on Industrial Informatics
• 影响因子: 12.3
• 作者: Kun Hu;Jin Ye;Wenzhan Song

Enhanced Cyber-Attack Detection in Intelligent Motor Drives: A Transfer Learning Approach With Convolutional Neural Networks

• DOI: 10.1109/jestie.2023.3346802
• 发表时间: 2024-04
• 期刊: IEEE Journal of Emerging and Selected Topics in Industrial Electronics
• 作者: Bowen Yang;Shushan Wu;Kun Hu;Jin Ye;Wenzhan Song;Ping Ma;Jianjun Shi;Peng Liu