Research Initiation Award:Secure measurement-based IP geolocation for Cloud Auditing

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Tennessee State University's Research Initiation Award entitled - Secure measurement-based IP Geolocation for Cloud Auditing - will enhance the cyber security research program at the university by investigating novel security problems in cloud auditing. The educational goal involves the integration of cloud auditing research in select undergraduate courses. Cloud computing is one of the most enticing technologies due to its scalable, flexible, and cost-efficient access to computing resources. However, the increased concentration of business data and computing power scales security risks as well. One of the recent security concerns is attributed to the lack of sufficient transparency in the operations of the cloud provider, leading to difficulties in cloud auditing. This project investigates a critical network mapping and measurement (NMM) technique, Secure IP Geolocation, to facilitate reliable cloud auditing. The overarching goal is to develop a methodology based on machine learning that will determine the geolocations of the cloud nodes containing user data with higher reliability, robustness and sensitivity than the current state-of-the art measurement-based IP geolocation techniques. Three separate but synergistic research goals are proposed: 1) develop a machine learning based approach to estimate the hop distances between arbitrary host pairs in complex network topologies that is accurate, scalable, timely, and does not require a significant measurement infrastructure; 2) develop a single node classifier using delay measurements and hop distances based on machine learning to detect forged latency results by adversarial clients in a cloud network; and 3) develop a fusion classifier which is scalable and independent of the network latency levels by training to pool latency and hop count data from multiple target nodes, allowing sufficient number of training examples at any latency level.