CRII: CPS: Data-Driven Cascading Failure Abstraction and Vulnerability Analysis in Cyber-Physical Systems

CRII：CPS：网络物理系统中数据驱动的级联故障抽象和漏洞分析

• 批准号: 1948550
• 负责人: Xiang Li
• 金额: $ 16.92万
• 依托单位: Santa Clara University
• 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: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948550&HistoricalAwards=false
• 关键词: CRII; CPS; Data; Driven; Cascading

The goal of this proposal is to establish a framework for cascading failure abstraction and vulnerability analysis in Cyber-Physical Systems (CPSs), empowered by data. CPSs are critical to modern society, however, they are vulnerable to attacks and failures. The failures in CPSs are more destructive because of cascading failure, which means that the failure of a part of the system can cause failure in the rest of the system and result in more severe damage. However, analysis of CPS vulnerability involving cascading failure is extremely challenging, mainly because 1) it’s hard to theoretically analyze the various physical processes happen in a cascade and 2) local diffusion models applied to the CPS network cannot capture the global impact of cascades. Using simpler cascade models derived from data as media, it is possible to have a deeper understanding of how CPSs are vulnerable to cascading failure. CPSs are gaining popularity and there is an urgent need to enhance its security, hence the proposed work will greatly benefit the society and of national interest. The project will provide opportunities for undergraduate students, underrepresented minority groups and women to research in some of the society’s most concerned fields like machine learning and security. Also, the outcomes of this work will be introduced in courses for undergraduate and graduate students and integrated into STEM outreach programs for K-12 students.The proposal goals will be achieved via four major tasks. (i) Develop simple meta-cascade models that preserve key features of cascades. (ii) Quantitatively measure vulnerability in CPSs with general damage functions defined on failed components and multiple meta-cascade models, as well as design protection schemes. (iii) Develop efficient sampling based dynamic approximation algorithms, which can run efficiently in billion-scale networks and can be stopped at any time with theoretical guarantees. The algorithms are widely applicable to optimization problems in large-scale networks and are essential in supporting the first two tasks. (iv) Implement and evaluate the proposed models and algorithms. The innovations in meta-cascade models can benefit research in CPS security, while the sampling-based algorithms can be a powerful new tool for a wide class of optimization problems in large-scale networks.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.

该提案的目标是建立一个框架，用于网络物理系统（CPS）中的级联故障抽象和脆弱性分析。CPS对现代社会至关重要，然而，它们容易受到攻击和失败。CPS中的故障更具破坏性，因为级联故障，这意味着系统的一部分的故障可以导致系统的其余部分的故障，并导致更严重的损害。然而，CPS脆弱性的分析涉及级联故障是极具挑战性的，主要是因为1）很难从理论上分析的各种物理过程中发生的级联和2）本地扩散模型应用于CPS网络不能捕捉级联的全球影响。使用更简单的级联模型，从数据作为媒体，它是可能有一个更深入的了解CPS是如何容易受到级联故障。CPS越来越受欢迎，迫切需要加强其安全性，因此拟议的工作将大大有利于社会和国家利益。该项目将为本科生、代表性不足的少数群体和女性提供机会，研究机器学习和安全等社会最关注的领域。此外，这项工作的成果将被引入到本科生和研究生的课程中，并融入到K-12学生的STEM推广计划中。提案目标将通过四个主要任务来实现。(i)开发保留级联关键特征的简单元级联模型。(ii)定量测量CPS的脆弱性与故障组件和多个元级联模型，以及设计保护方案定义的一般损害函数。(iii)开发高效的基于采样的动态近似算法，该算法可以在十亿级网络中高效运行，并且可以在理论上保证随时停止。这些算法广泛适用于大规模网络中的优化问题，并且在支持前两个任务方面是必不可少的。(iv)实现和评估所提出的模型和算法。元级联模型的创新可以使CPS安全研究受益，而基于采样的算法可以成为大规模网络中各种优化问题的强大新工具。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，被认为值得支持。

项目成果

Trust-Based Communities for Smart Grid Security and Privacy

基于信任的智能电网安全和隐私社区

• 发表时间: 2023
• 期刊: Lecture notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering
• 作者: Park, Seohyun;Li, Xiang;Liu, Yuhong
• 通讯作者: Liu, Yuhong

Time-Constrained Adaptive Influence Maximization

• DOI: 10.1109/tcss.2020.3032616
• 发表时间: 2021-02-01
• 期刊: IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
• 影响因子: 5
• 作者: Tong, Guangmo;Wang, Ruiqi;Li, Xiang
• 通讯作者: Li, Xiang

Identification of Key Components After Unintentional Failures for Cascading Failure Protection

• DOI: 10.1109/tnse.2022.3225459
• 发表时间: 2023-03
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Xiang Li;Tianyi Pan;Kai Pan

Power Grid Cascading Failure Prediction Based on Transformer

基于变压器的电网连锁故障预测

• DOI: 10.1007/978-3-030-91434-9_15
• 发表时间: 2021
• 期刊: Lecture notes in computer science
• 作者: Zhou, Tianxin;Li, Xiang;Lu, Haibing
• 通讯作者: Lu, Haibing

A short proof for stronger version of DS decomposition in set function optimization

• DOI: 10.1007/s10878-020-00639-4
• 发表时间: 2020-08
• 期刊: Journal of Combinatorial Optimization
• 影响因子: 1
• 作者: Xiang Li;Hongbin George Du