EAGER: Collaborative: Algorithmic Framework for Anomaly Detection in Interdependent Networks

EAGER：协作：相互依赖网络中异常检测的算法框架

• 批准号: 1646890
• 负责人: Nina Fefferman
• 金额: $ 9.99万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646890&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Algorithmic; Framework; Anomaly

Modern critical infrastructure relies on successful interdependent function among many different types of networks. For example, the Internet depends on access to the power grid, which in turn depends on the power-grid communication network and the energy production network. For this reason, network science researchers have begun examining the robustness of critical infrastructure as a network of networks, or a multilayer network. Research in network anomaly detection systems has focused on single network structures (specifically, the Internet as a single network). Among these methods, some promising detection algorithms rely on decentralized and distributed coordination among many participants, improving meaningfully over results from independent parallel and centralized algorithms. The project involves rigorous analysis of the different challenges and opportunities for anomaly detection posed by multilayer networks relative to single network structures, with a particular focus on how cross-layer information can be effectively used to improve both efficiency and detection as well as how cross-layer threats can create vulnerabilities. The project develops a general framework that can be used in multiple applications to detect large-scale threats to information flow for enhanced security. This has the potential for significant benefit to society through its contribution to enhanced resiliency in the nation's cyber infrastructure and other interdependent critical infrastructure such as the power grid. The combination of concepts and ideas from the cybersecurity community with the network science community will help researchers in both fields to better understand the realistic problems and be aware of each other's problems, results, and techniques.

现代关键基础架构依赖于许多不同类型的网络之间的成功相互依存功能。例如，互联网取决于对电网的访问，这又取决于电网通信网络和能源生产网络。因此，网络科学研究人员已经开始研究关键基础架构作为网络或多层网络的鲁棒性。网络异常检测系统的研究集中在单个网络结构上（特别是Internet作为单个网络）。在这些方法中，一些有希望的检测算法依赖于许多参与者的分散和分布配位，从而有意义地改善了独立平行和集中式算法的结果。该项目涉及对多层网络相对于单个网络结构提出的不同挑战和机遇的严格分析，并特别关注如何有效地使用跨层信息来提高效率和检测以及跨层威胁如何创造脆弱性。该项目开发了一个通用框架，可用于多个应用程序中，以检测对信息流的大规模威胁，以增强安全性。通过贡献其对国家网络基础设施的弹性和其他相互依存的批判基础设施（例如电网）的贡献，这可能会给社会带来重大利益。网络安全社区与网络科学界的概念和思想的结合将帮助两个领域的研究人员更好地了解现实的问题，并意识到彼此的问题，结果和技术。

项目成果

Anomaly detection through information sharing under different topologies

通过不同拓扑下的信息共享进行异常检测

• DOI: 10.1186/s13635-017-0056-5
• 发表时间: 2017
• 期刊: EURASIP Journal on Information Security
• 影响因子: 3.6
• 作者: Gallos, Lazaros K.;Korczyński, Maciej;Fefferman, Nina H.
• 通讯作者: Fefferman, Nina H.

Propinquity drives the emergence of network structure and density

• DOI: 10.1073/pnas.1900219116
• 发表时间: 2019-09
• 期刊: Proceedings of the National Academy of Sciences
• 作者: L. Gallos;S. Havlin;H. Stanley;N. Fefferman