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: 1701394
• 负责人: Zhuo Lu
• 金额: $ 6.55万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-05 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1701394&HistoricalAwards=false
• 关键词: CRII; NeTS; Proactive; Perspective; Preventing; 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，以挫败针对无线网络信息推断信息的恶意努力。将制定和测试新的主动反推理策略，以使网络更具动态性和更少的推理敏感性。该项目涉及在网络推论/反推断理论和实验活动中对本科和研究生的培训，并有望显着影响关键任务 - 无线网络的安全设计。网络推断是从网络测量中推断网络统计信息和属性信息的有效方法。网络推断已实现了广泛的应用程序，例如网络监视，管理和诊断；但是，在某些情况下，例如用于军事应用的无线网络，掩盖此信息以防止推理恶意意图很重要。迄今为止，无线网络中反推理的策略已被忽略和探索。这项努力的核心目标是通过探索主动策略的基本方面来解决当前理解的差距，这些方面将实现广泛的反推理应用。具体目标是（1）开发一个理论框架来分析积极主动策略的行为，从而增强网络动态以实现反推理； （2）揭示了基于网络动态的反推断策略的影响和成本之间的基本关系； （3）为网络应用程序实施有效的反推理设计。为了实现这些目标，欺骗流量和路由变化策略都将用于诱导网络中的更高动态，以使推理不准确。此外，这些策略的成本效益影响将通过从优化的动态网络设计转移来检查。