Measuring ISP topologies with rocketfuel
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Towards IP geolocation using delay and topology measurements
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Avoiding traceroute anomalies with Paris traceroute
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Constraint-based geolocation of internet hosts
IEEE/ACM Transactions on Networking (TON)
Defense against spoofed IP traffic using hop-count filtering
IEEE/ACM Transactions on Networking (TON)
Operating system support for planetary-scale network services
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
iPlane: an information plane for distributed services
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
How DNS misnaming distorts internet topology mapping
ATEC '06 Proceedings of the annual conference on USENIX '06 Annual Technical Conference
Network discovery from passive measurements
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Census and survey of the visible internet
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Traceroute probe method and forward IP path inference
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
All of Nonparametric Statistics
All of Nonparametric Statistics
Octant: a comprehensive framework for the geolocalization of internet hosts
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
On the geographic location of Internet resources
IEEE Journal on Selected Areas in Communications
How to tell an airport from a home: techniques and applications
Hotnets-IX Proceedings of the 9th ACM SIGCOMM Workshop on Hot Topics in Networks
Eyeball ASes: from geography to connectivity
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Dude, where’s that IP?: circumventing measurement-based IP geolocation
USENIX Security'10 Proceedings of the 19th USENIX conference on Security
On the prevalence and characteristics of MPLS deployments in the open internet
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
A structural approach for PoP geo-location
Computer Networks: The International Journal of Computer and Telecommunications Networking
Geolocating IP addresses in cellular data networks
PAM'12 Proceedings of the 13th international conference on Passive and Active Measurement
Towards a collaborative geosocial analysis workbench
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Posit: a lightweight approach for IP geolocation
ACM SIGMETRICS Performance Evaluation Review
RiskRoute: a framework for mitigating network outage threats
Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
A lightweight mechanism for detection of cache pollution attacks in Named Data Networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
DataTraffic Monitoring and Analysis
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The ability to pinpoint the geographic location of IP hosts is compelling for applications such as on-line advertising and network attack diagnosis. While prior methods can accurately identify the location of hosts in some regions of the Internet, they produce erroneous results when the delay or topology measurement on which they are based is limited. The hypothesis of our work is that the accuracy of IP geolocation can be improved through the creation of a flexible analytic framework that accommodates different types of geolocation information. In this paper, we describe a new framework for IP geolocation that reduces to a machine-learning classification problem. Our methodology considers a set of lightweight measurements from a set of known monitors to a target, and then classifies the location of that target based on the most probable geographic region given probability densities learned from a training set. For this study, we employ a Naive Bayes framework that has low computational complexity and enables additional environmental information to be easily added to enhance the classification process. To demonstrate the feasibility and accuracy of our approach, we test IP geolocation on over 16,000 routers given ping measurements from 78 monitors with known geographic placement. Our results show that the simple application of our method improves geolocation accuracy for over 96% of the nodes identified in our data set, with on average accuracy 70 miles closer to the true geographic location versus prior constraint-based geolocation. These results highlight the promise of our method and indicate how future expansion of the classifier can lead to further improvements in geolocation accuracy.