A Street-Level IP Geolocation Method Based on Delay-Distance Correlation and Multilayered Common Routers

Abstract

The geographical locations of smart devices can help in providing authentication information between multimedia content providers and users in 5G networks. The IP geolocation methods can help in estimating the geographical location of these smart devices. The two key assumptions of existing IP geolocation methods are as follows: (1) the smallest relative delay comes from the nearest host; (2) the distance between hosts which share the closest common routers is smaller than others. However, the two assumptions are not always true in weakly connected networks, which may affect accuracy. We propose a novel street-level IP geolocation algorithm (Corr-SLG), which is based on the delay-distance correlation and multilayered common routers. The first key idea of Corr-SLG is to divide landmarks into different groups based on relative-delay-distance correlation. Different from previous methods, Corr-SLG geolocates the host based on the largest relative delay for the strongly negatively correlated groups. The second key idea is to introduce the landmarks which share multilayered common routers into the geolocation process, instead of only relying on the closest common routers. Besides, to increase the number of landmarks, a new street-level landmark collection method called WiFi landmark is also presented in this paper. The experiments in one province capital city of China, Zhengzhou, show that Corr-SLG can improve the geolocation accuracy remarkably in a real-world network.

GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework

“…IP after the probing host [2] obtains the measurement data (e.g., delay) to the target IP and landmarks [2,5]. Landmarks are IP addresses with known locations [2].…”

“…IP after the probing host [2] obtains the measurement data (e.g., delay) to the target IP and landmarks [2,5]. Landmarks are IP addresses with known locations [2]. Generally, existing measurement-based fine-grained IP geolocation methods can be categorized into two kinds: rule-based and deep learning-based methods (referred as learning-based methods in this paper).…”

“…Generally, existing measurement-based fine-grained IP geolocation methods can be categorized into two kinds: rule-based and deep learning-based methods (referred as learning-based methods in this paper). For rule-based methods like SLG [5] and Corr-SLG [2], they assume that most hosts in one computer network are following some delay-distance rules. These hypothetical rules are proposed by human experts based on their observations.…”

“…I P geolocation is to obtain the geographic location (geolocation) of an IP address [1,2]. It is widely used in localized online advertisement, location-based content restriction, cybercrime tracking, and so on [1,3,4].…”

“…The median error distances of fine-grained IP geolocation methods are usually less than 10 kilometers. Though there are several finegrained IP geolocation methods like SLG [5], Checkin-geo [6], Corr-SLG [2] and MLP-Geo [7], they stills face many challenging problems. In this paper, we mainly focus on improving the generalization capabilities of measurement-based fine-grained IP geolocation in different computer networks.…”