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

基本信息

批准号：
2019340
负责人：
Peng Liu
金额：
$ 30万
依托单位：
Pennsylvania State Univ University Park
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=2019340&HistoricalAwards=false
关键词：
Collaborative Research SaTC CORE Medium

项目摘要

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.

在现代的多阶段制造系统中，随着软件定义的自动化和控制以及对网络跨网络的制造资产的监视，对网络攻击的暴露也会增长。网络威胁可能会损害制造资产（制造系统和流程，机床，制造零件）的完整性，降低制造生产率并提高成本。仅在网络空间中只能观察到一些网络威胁，包括完整性攻击，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，探索物理信号以及网络信号有很大的机会来推进值得信赖的制造系统研究和设计。该项目汇集了制造系统，计算机安全和电气驱动器的领先研究人员，以开发和演示一种新的方法和工具，用于在多阶段制造系统中进行网络威胁检测和诊断。网络安全工具将监视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点分析来执行网络威胁性检测和根源诊断。目的是在早期阶段预防和减轻潜在危害，积极主动和预测性维护以及对策。该项目试图集成和分析流程和质量信号，以及来自多阶段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。这种新的系统方法希望捕获传统的网络安全方法可能无法捕获的新的网络威胁，尤其是数据完整性攻击。整合网络和物理信号的拟议数据分析和方法将对多阶段制造系统中的网络威胁检测和诊断进行基本理解，并可以广泛地应用于其他网络物理系统。这奖反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的crietia crietia crietia crietia crietia crietia criteria criperia criperia crietia crietia criperia criperia criperia criperia recteria recteria rection the Appliatia奖。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Physical Devices-Agnostic Hybrid Fuzzing of IoT Firmware

DOI：
10.1109/jiot.2023.3303780
发表时间：
2023-12
期刊：
IEEE Internet of Things Journal
影响因子：
10.6
作者：
Lingyun Situ;Chi Zhang;Le Guan;Zhiqiang Zuo;Linzhang Wang;Xuandong Li;Peng Liu;Jin Shi
通讯作者：
Lingyun Situ;Chi Zhang;Le Guan;Zhiqiang Zuo;Linzhang Wang;Xuandong Li;Peng Liu;Jin Shi

Identifying Channel Related Vulnerabilities in Zephyr Firmware

DOI：
10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00055
发表时间：
2022-08
期刊：
2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)
影响因子：
0
作者：
Devansh Rajgarhia;Peng Liu;S. Sural
通讯作者：
Devansh Rajgarhia;Peng Liu;S. Sural

A co-design adaptive defense scheme with bounded security damages against Heartbleed-like attacks

一种针对类似 Heartbleed 的攻击具有有限安全损害的协同设计自适应防御方案

DOI：
10.1109/tifs.2021.3113512
发表时间：
2021
期刊：
IEEE Transactions on Information Forensics and Security
影响因子：
6.8
作者：
Hu, Zhisheng;Chen, Ping;Zhu, Minghui;Liu, Peng
通讯作者：
Liu, Peng

Recompose Event Sequences vs. Predict Next Events: A Novel Anomaly Detection Approach for Discrete Event Logs

DOI：
10.1145/3433210.3453098
发表时间：
2020-12
期刊：
Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security
影响因子：
0
作者：
Lun-Pin Yuan;Peng Liu;Sencun Zhu
通讯作者：
Lun-Pin Yuan;Peng Liu;Sencun Zhu

Semantics-Preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection