TWC: Small: Collaborative: Multi-Layer Approaches for Securing Enhanced AMI Networks against Traffic Analysis Attacks

TWC：小型：协作：保护增强型 AMI 网络免受流量分析攻击的多层方法

• 批准号: 1617394
• 负责人: Husheng Li
• 金额: $ 10.79万
• 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=1617394&HistoricalAwards=false
• 关键词: TWC; Small; Collaborative; Multi; Layer

The U.S. power grid is being replaced with a smart grid, a complex network of intelligent electronic devices, distributed generators, and dispersed loads, which requires communication networks for management and coordination. Advanced metering infrastructure (AMI) networks are one part of the smart grid to provide two-way communications between smart meters at the consumers' side and the utility companies. AMI networks allow utilities to collect power consumption data at high frequency rates. However, it needs too much communication bandwidth for smart meters to frequently send power consumption data even when the power consumption does not change. Since using cellular networks is one of the best options to AMI networks, the cost of sending this large amount of data is prohibitive. This project considers enhanced AMI networks, where the meters send power consumption data only when there is a significant change. This can significantly reduce the amount of bandwidth needed for sending the power consumption data; however, it creates a new privacy problem. Practical experiment results have confirmed that by observing the data transmission rate and using traffic analysis techniques, the attackers can infer sensitive information about consumers. Therefore, this new privacy problem must be studied and addressed, and strong countermeasures should be developed.The proposed research systematically combines efforts from privacy, networking, and communication communities. The project promotes a research program designed to: (a) develop schemes for countering traffic analysis in AMI networks by considering different network and adversary models; (b) quantitatively measure the privacy protection provided by the schemes; and (c) evaluate the schemes in a prototype system for validating the proposed research and enabling hands-on experience for both undergraduate and graduate students. The project will significantly contribute to the research on smart grid, as well as computer system security and privacy. The proposed research will lead to a body of knowledge that can be leveraged by the designers of other networks. The proposed project also lends itself to teaching, training and learning of students. A new graduate course focusing on security and privacy aspects of smart grid will be developed. The achievement of the proposed research will be disseminated to academic community and industry via academic conferences and industrial connections.

美国电网正在被智能电网取代，这是一个由智能电子设备、分布式发电机和分散负载组成的复杂网络，需要通信网络进行管理和协调。高级计量基础设施（AMI）网络是智能电网的一部分，提供消费者侧智能电表和公用事业公司之间的双向通信。 AMI 网络允许公用事业公司以高频率收集功耗数据。然而，即使在用电量没有变化的情况下，智能电表也需要频繁发送用电数据，需要过多的通信带宽。由于使用蜂窝网络是 AMI 网络的最佳选择之一，因此发送如此大量数据的成本令人望而却步。该项目考虑增强型 AMI 网络，其中电表仅在发生重大变化时才发送功耗数据。这可以显着减少发送功耗数据所需的带宽量；然而，它带来了新的隐私问题。实际实验结果证实，通过观察数据传输速率并利用流量分析技术，攻击者可以推断出消费者的敏感信息。因此，必须研究和解决这一新的隐私问题，并制定强有力的对策。 本研究系统地结合了隐私、网络和通信社区的努力。该项目推动一项研究计划，旨在： (a) 通过考虑不同的网络和对手模型，制定反 AMI 网络流量分析的方案； (b) 定量衡量该计划提供的隐私保护； (c) 评估原型系统中的方案，以验证拟议的研究并为本科生和研究生提供实践经验。该项目将为智能电网以及计算机系统安全和隐私的研究做出重大贡献。拟议的研究将产生可供其他网络的设计者利用的知识体系。拟议的项目也适用于学生的教学、培训和学习。将开发一门新的研究生课程，重点关注智能电网的安全和隐私方面。拟议研究的成果将通过学术会议和行业联系向学术界和行业传播。