EAGER: Collaborative Research:Blockchain Graphs as Testbeds of Power Grid Resilience and Functionality Metrics

EAGER：协作研究：区块链图作为电网弹性和功能指标的测试平台

• 批准号: 2039701
• 负责人: Jie Zhang
• 金额: $ 15万
• 依托单位: University of Texas at Dallas
• 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: 2020-11-15 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039701&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Blockchain; Graphs

Rapid adoption of renewable energy, growth of distributed generation technologies and microgrid deployment, widespread implementations of IoT devices and associated cybersecurity concerns have led to an ever expanding risk landscape and have, in turn, shifted the focus from energy system protection to resilience. Furthermore, the past two decades have seen a consistently increasing interest in the application of tools developed in the interdisciplinary field of complex network analysis to enhance our understanding of functionality, security, and reliability of power systems and critical infrastructures in which power grid networks serve as an integral constituent. Such systems are intrinsically interconnected and interdependent, and as a result, failure of one component due to manmade and natural cause, e.g., adverse weather, aging, and intentional attacks such as terrorism, can lead to a catastrophic cascade of failures. However, one of the key obstacles for developing systematic and reliable assessment of power grid and critical infrastructure resiliency under various threats is unavailability of ground truth data on system response to adversaries due to data security, privacy and confidentiality. Motivated by these problems rooted in the analysis of modern power grids but inherent to many other complex network systems and given public availability of all blockchain transaction transactions, we propose to use blockchain graphs as a test-bed for validation of functionality and resilience metrics on complex power grid networks.Exploring power grid sensitivity to targeted attacks and failures in scenarios allowing for validation of any proposed methodology against ground truth is the key toward developing reliable risk mitigation strategies for power systems. The novel research approaches to be developed in this project aim to lay foundations for deeper knowledge integration and transfer across power systems, finance, social sciences and other disciplines by utilizing graph-theoretic analysis and adopting a common mathematical language of computational topology. By promoting new synergistic research initiatives, the project will further advance knowledge creation not only on power grids and blockchain but on modern complex networks, from brain connectome to social media platforms to telecommunication-- thereby facilitating resilience and sustainability of our society.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.

快速采用可再生能源，分布式发电技术的增长和微电网部署，物联网设备的广泛实施以及相关的网络安全问题导致了不断扩大的风险景观，进而将重点从能源系统的保护转移到了弹性。此外，在过去的二十年中，人们对在复杂网络分析的跨学科领域开发的工具的应用持续不断提高，以增强我们对功率系统的功能，安全性和关键基础设施的理解，在这些工具中，电力网络是一个积分概要性的。这样的系统本质上是相互联系的和相互依存的，因此，由于人为和自然原因而导致的一个组成部分失败，例如不利的天气，衰老和诸如恐怖主义之类的故意攻击，可能会导致灾难性的失败级联。但是，在各种威胁下，在各种威胁下对电网和关键基础设施的弹性进行系统评估的关键障碍之一是，由于数据安全性，隐私和机密性而导致的对手的系统响应无法获得地面真相数据。由这些问题植根于现代电力电网的分析但许多其他复杂网络系统固有的问题并赋予所有区块链交易交易的可用性的动机，我们建议使用区块链图作为验证功能性和弹性指标的测试床，以对复杂的功率网络进行验证，以实现对目标的攻击，以实现对目标的攻击，以实现对目标的影响。电力系统的风险缓解策略。该项目中要开发的新型研究方法旨在通过利用图理论分析并采用计算拓扑的常见数学语言来奠定深层知识整合和转移的基础。通过促进新的协同研究计划，该项目将不仅会进一步推进知识网格和区块链的知识创建，而且还将在现代复杂的网络上，从大脑连接到社交媒体平台到电信社会的现代复杂网络，从而促进我们的社会的复原力和可持续性，从而促进了NSF的法定任务，反映出值得通过评估的人的知识群体的范围，这一奖项被认为是众所周知的范围。

项目成果

Topological Relational Learning on Graphs

• 发表时间: 2021-10
• 作者: Yuzhou Chen;Baris Coskunuzer;Y. Gel

Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT

• DOI: 10.48550/arxiv.2211.13708
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: C. Akcora;Murat Kantarcioglu;Y. Gel;Baris Coskunuzer

Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains

• 发表时间: 2022
• 作者: Kiarash Shamsi;Friedhelm Victor;Murat Kantarcioglu;Y. Gel;C. Akcora

Tlife-GDN: Detecting and Forecasting Spatio-Temporal Anomalies via Persistent Homology and Geometric Deep Learning

Tlife-GDN：通过持久同源性和几何深度学习检测和预测时空异常

• 发表时间: 2022
• 期刊: Pacific-Asia Conference on Knowledge Discovery and Data Mining
• 作者: Zhen, Zhiwei;Chen, Yuzhou;Segovia-Dominguez, Ignacio;Gel, Yulia R.
• 通讯作者: Gel, Yulia R.

Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks

• DOI: 10.1007/978-3-030-86486-6_48
• 发表时间: 2021-01-01
• 期刊: MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
• 作者: Ofori-Boateng, D.;Dominguez, I. Segovia;Gel, Y. R.
• 通讯作者: Gel, Y. R.