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

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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.