CRII: NeTS: A Proactive Perspective on Preventing Network Inference: Shifting from Optimized to Dynamic Wireless Network Design

CRII：NeTS：防止网络推理的主动视角：从优化到动态无线网络设计的转变

• 批准号: 1464114
• 负责人: Zhuo Lu
• 金额: $ 10万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464114&HistoricalAwards=false
• 关键词: CRII; NeTS; Proactive; Perspective; Preventing

This project will design strategies to thwart malicious efforts targeting the inference of information from wireless networks. New proactive anti-inference strategies will be developed and tested to make networks more dynamic and less inference-susceptible. The project involves the training of undergraduate and graduate students in network inference/anti-inference theoretical and experimental activities, and is expected to significantly impact the security design of mission-critical wireless networks.Network inference is an effective way to infer the information of network statistics and properties from network measurements. Network inference has enabled a wide range of applications, such as network surveillance, management and diagnosis; however, in certain circumstances, such as wireless networks for military applications, it is important to mask this information to prevent the use of inference for malicious intent. To date, strategies for anti-inference in wireless networks have been overlooked and under-explored. The central goal of this effort is to address the current gap in understanding by exploring the fundamental aspects of proactive strategies that will enable a wide range of anti-inference applications. Specific objectives are to (1) develop a theoretical framework to analyze the behaviors of proactive strategies that enhance network dynamics to achieve anti-inference; (2) reveal the fundamental relationship between the impacts and costs of network dynamics based anti-inference strategies; and (3) implement efficient anti-inference design for network applications. To accomplish these objectives, both deception traffic and routing changing strategies will be utilized to induce higher dynamics in the network to make inference inaccurate. In addition, the cost-benefit impact of these strategies will be examined by shifting from optimized to dynamic network design.

该项目将设计策略，以挫败针对从无线网络推断信息的恶意努力。将开发和测试新的主动反推理策略，以使网络更动态，更不容易推理。该项目涉及对本科生和研究生的网络推理/反推理理论和实验活动的培训，预计将对关键任务无线网络的安全设计产生重大影响。网络推理是从网络测量数据推断网络统计和属性信息的有效方法。网络推理已经实现了广泛的应用，例如网络监视、管理和诊断；然而，在某些环境中，例如用于军事应用的无线网络，重要的是屏蔽这些信息以防止恶意使用推理。到目前为止，无线网络中的反干扰策略一直被忽视和探索不足。这一努力的中心目标是通过探索能够广泛应用反推理的主动战略的基本方面来解决目前在理解方面的差距。具体目标是(1)建立一个理论框架来分析增强网络动态以实现反推理的主动策略的行为；(2)揭示基于网络动态的反推理策略的影响与代价之间的基本关系；(3)为网络应用实现高效的反推理设计。为了实现这些目标，欺骗流量和路由改变策略都将被用来在网络中诱导更高的动态以使推理不准确。此外，还将通过从优化网络设计转变为动态网络设计来检验这些策略的成本效益影响。