IP Infrastructure Geolocation

Abstract

Physical network maps are important to critical infrastructure defense and planning. Current state-of-the-art network infrastructure geolocation relies on Domain Name System (DNS) inferences. However, not only is using the DNS relatively inaccurate for infrastructure geolocation, many router interfaces lack DNS name entries. We adapt the technique of Wang et al. to send traceroute probes from distributed vantage points, and approximate a target's location by finding the nearest landmark. To evaluate the technique's performance, we geolocate router interfaces previously geolocated via DNS-based router positioning (DRoP). Our results show that 50% of the targets have error distances greater than 2,400 km; however, 75% of the nearest landmark predictions are less than 5 ms distant. We find that geolocation accuracy is insensitive to vantage point location, while the use of more vantage points improves accuracy. To better understand these results, we use Constraint based Geolocation (CBG) on a subset of DRoP predictions. Forty-six percent of 4,638 DRoP location inferences are in regions outside the feasible physical boundaries imposed by CBG and 56% are 1,800 km away from the CBG centroid. Our findings suggest that our methodology can supplement prior work to not only geolocate infrastructure without DNS names, but also improve accuracy.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2015
Accession Number
ADA620814

Entities

People

  • Guan Y. Cai

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Accuracy
  • Boundaries
  • California
  • Computer Communications
  • Computer Science
  • Computers
  • Continents
  • Data Analysis
  • Data Centers
  • Errors
  • Geolocation
  • Global Positioning Systems
  • Infrastructure
  • Network Protocols
  • New York
  • North America
  • United States

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Computer Vision.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms