Abstract

IP geolocation determines geographical location by the IP address of Internet hosts. IP geolocation is widely used by target advertising, online fraud detection, cyber-attacks attribution and so on. It has gained much more attentions in these years since more and more physical devices are connected to cyberspace. Most geolocation methods cannot resolve the geolocation accuracy for those devices with few landmarks around. In this paper, we propose a novel geolocation approach that is based on common routers as secondary landmarks (Common Routers-based Geolocation, CRG). We search plenty of common routers by topology discovery among web server landmarks. We use statistical learning to study localized (delay, hop)-distance correlation and locate these common routers. We locate the accurate positions of common routers and convert them as secondary landmarks to help improve the feasibility of our geolocation system in areas that landmarks are sparsely distributed. We manage to improve the geolocation accuracy and decrease the maximum geolocation error compared to one of the state-of-the-art geolocation methods. At the end of this paper, we discuss the reason of the efficiency of our method and our future research.

Highlights

  • We propose a method that discovers intermediate routers (stable but with few geographical information) and uses them as secondary landmarks to increase the granularity and stability of IP geolocation results

  • IP geolocation aims to determine the geographical location of an Internet host by its IP address (Muir and Oorschot 2009)

  • We propose a method that discovers intermediate routers and uses them as secondary landmarks to increase the granularity and stability of IP geolocation results

Read more

Summary

Introduction

We propose a method that discovers intermediate routers (stable but with few geographical information) and uses them as secondary landmarks to increase the granularity and stability of IP geolocation results. Instead of latency vector and pinning, CBG uses geographical distance and multilateration to locate target host. Laki et al (2011) propose a statistical model that associates network latencies to geographical distance range and use maximum likelihood to estimate most possible location.

Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call