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: 1646856
• 负责人: Rebecca Wright
• 金额: $ 20.01万
• 依托单位: Rutgers University New Brunswick
• 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=1646856&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.

现代关键基础设施依赖于许多不同类型网络之间成功的相互依存功能。例如，互联网依赖于对电网的接入，而电网又依赖于电网通信网络和能源生产网络。出于这个原因，网络科学研究人员已经开始研究关键基础设施作为网络的网络或多层网络的鲁棒性。网络异常检测系统的研究主要集中在单个网络结构上（具体来说，互联网作为一个单一的网络）。在这些方法中，一些有前途的检测算法依赖于许多参与者之间的分散和分布式协调，比独立并行和集中式算法的结果有意义地改进。该项目涉及对多层网络相对于单一网络结构所带来的异常检测的不同挑战和机遇的严格分析，特别关注如何有效地使用跨层信息来提高效率和检测，以及跨层威胁如何产生漏洞。该项目开发了一个通用框架，可用于多个应用程序，以检测对信息流的大规模威胁，以增强安全性。这有可能通过其对增强国家网络基础设施和电网等其他相互依赖的关键基础设施的弹性的贡献，为社会带来重大利益。网络安全社区与网络科学社区的概念和想法的结合将有助于这两个领域的研究人员更好地理解现实问题，并了解彼此的问题，结果和技术。

项目成果

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

A Case Study in Tailoring a Bio-InspiredCyber-Security Algorithm: DesigningAnomaly Detection for Multilayer Networks

定制仿生网络安全算法的案例研究：设计多层网络异常检测

• DOI: 10.13052/jcsm2245-1439.815
• 发表时间: 2018
• 期刊: Journal of Cyber Security and Mobility
• 作者: Suárez, Gonzalo P.;Suárez, Gonzalo P.;Gallos, Lazaros K.;Fefferman, Nina H.;Fefferman, Nina H.
• 通讯作者: Fefferman, Nina H.

A Case Study in Tailoring a Bio-Inspired Cyber-Security Algorithm: Designing Anomaly Detection for Multilayer Networks

定制仿生网络安全算法的案例研究：为多层网络设计异常检测

• DOI: 10.1109/spw.2018.00045
• 发表时间: 2018
• 期刊: 2018 IEEE Security and Privacy Workshops (SPW
• 作者: Suarez, Gonzalo;Gallos, Lazaros;Fefferman, Nina
• 通讯作者: Fefferman, Nina

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.