GeoCET: Accurate IP Geolocation via Constraint-Based Elliptical Trajectories

The geographical location of the IP device is crucial for many network security applications, such as location-aware authentication, fraud prevention, and security-sensitive forensics. Since most data mining-based methods are subject to the privacy protection policies, the delay-based measurement methods have broader application prospects. However, these methodologies are relying on heavyweight traffic on networks and high deployment costs. Besides, the worst case errors in estimation made by delay-based measurement methods render them ineffective. In this paper, we propose an accurate IP geolocation approach called GeoCET. This methodology only requires a small number of one-way delays (OWDs) to locate the targets, combining with elliptical trajectory constraints and maximum log-likelihood estimation technique. We introduce polynomial regression to fit the delay-distance model and enhance the accuracy of the localization. To evaluate GeoCET, we leverage real-world data which come from China, India, Western United States, and Central Europe. Experimental results demonstrate that GeoCET performs better for all existing measurement-based IP geolocation methodologies.