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.

美国电网被智能电网替换，这是一个复杂的智能电子设备，分布式发电机和分散负载的复杂网络，这需要用于管理和协调的通信网络。高级计量基础架构（AMI）网络是智能电网的一部分，可以在消费者方面和公用事业公司之间提供双向通信。 AMI网络允许公用事业以高频率收集功耗数据。但是，即使功耗不变，智能电表也需要太多的通信带宽，以便经常发送功耗数据。由于使用蜂窝网络是AMI网络的最佳选择之一，因此发送大量数据的成本是过于良好的。该项目考虑了增强的AMI网络，该网络仅在发生重大变化时才发送功耗数据。这可以大大减少发送功耗数据所需的带宽量；但是，它会产生一个新的隐私问题。实际实验结果已经证实，通过观察数据传输速率并使用交通分析技术，攻击者可以推断有关消费者的敏感信息。因此，必须研究和解决这个新的隐私问题，并应开发强大的对策。拟议的研究系统地结合了隐私，网络和通信社区的努力。该项目促进了一项研究计划：（a）通过考虑不同的网络和对手模型来制定针对AMI网络中流量分析的方案； （b）定量衡量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动手体验。该项目将极大地有助于智能电网以及计算机系统安全和隐私的研究。拟议的研究将导致其他网络设计师可以利用的知识体系。拟议的项目还适合对学生的教学，培训和学习。将开发一门关注安全性和智能电网隐私方面的新研究生课程。拟议研究的成就将通过学术会议和工业联系传播到学术界和行业。