Network Topology Boundary Routing IP Identification for IP Geolocation

Yuan, Fuxiang; Liu, Fenlin; Xu, Rui; Liu, Yan; Luo, Xiangyang

doi:10.1007/978-3-030-57881-7_47

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…City-level IP geolocation has developed into a relatively mature stage. e main methods include GeoPing [7], CBG [8], TBG [9], Octant [10], Structon [11], GeoGet [12], Chen-Geo [13], PLAG [14], Yuan-Geo [15], and RNBG [5]. is paper mainly discusses street-level IP geolocation methods.…”

Section: Introductionmentioning

confidence: 99%

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

Ding

Zhao²,

Luo³

2021

Security and Communication Networks

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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.

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Section: Introductionmentioning

confidence: 99%

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

Ding

Zhao²,

Luo³

2021

Security and Communication Networks

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“…Ref. [11] identifies the boundary IP based on the difference in single-hop delay of the path and the difference in router hostname of different cities. If the difference in single-hop delay is noticeable, the boundary IP can be identified by comparing each single-hop delay with the target city delay threshold.…”

Section: Introductionmentioning

confidence: 99%

A City-Level Boundary Nodes Identification Algorithm Based on Bidirectional Approaching

2021

KSII TIIS

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Existing city-level boundary nodes identification methods need to locate all IP addresses on the path to differentiate which IP is the boundary node. However, these methods are susceptible to time-delay, the accuracy of location information and other factors, and the resource consumption of locating all IPes is tremendous. To improve the recognition rate and reduce the locating cost, this paper proposes an algorithm for city-level boundary node identification based on bidirectional approaching. Different from the existing methods based on time-delay information and location results, the proposed algorithm uses topological analysis to construct a set of candidate boundary nodes and then identifies the boundary nodes. The proposed algorithm can identify the boundary of the target city network without highprecision location information and dramatically reduces resource consumption compared with the traditional algorithm. Meanwhile, it can label some errors in the existing IP address database. Based on 45,182,326 measurement results from Zhengzhou, Chengdu and Hangzhou in China and New York, Los Angeles and Dallas in the United States, the experimental results show that: The algorithm can accurately identify the city boundary nodes using only 20.33% location resources, and more than 80.29% of the boundary nodes can be mined with a precision of more than 70.73%.

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Network Topology Boundary Routing IP Identification for IP Geolocation

Cited by 2 publications

References 21 publications

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

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

A City-Level Boundary Nodes Identification Algorithm Based on Bidirectional Approaching

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