An Algorithm of City-Level Landmark Mining Based on Internet Forum

Zhu, Guang; Luo, Xiangyang; Liu, Fenlin; Chen, Jingning

doi:10.1109/nbis.2015.46

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…The reliability of the verification results cannot be guaranteed if only using single database. After Structon, the researchers use multiple database to verify the provincial and city-level locations and use zip codes for further verification [13][14][15]. Those strategies improved the accuracy of landmarks obviously but did not reach the requirement of street-level landmarks evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…On the basis of [12], Jiang H et al [13] exclude the cloud services landmarks according to cloud service providers' IP address (such as Amazon AWS, Rackspace Hosting, and other top cloud providers), exclude hosting landmarks whose organizations in Whois do not contain specific keywords such as "university", "academy", or "institution", and use MaxMind database to verify landmarks' city location. E-GeoTrack algorithm [14] based on voting strategy and implicit information in nearest router was proposed to evaluate Internet forum landmarks obtained from a web page. Above methods can only evaluate the accuracy of city location, and there are large errors in fine-grained geolocation using evaluated landmarks.…”

Section: Introductionmentioning

confidence: 99%

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Street-Level Landmark Evaluation Based on Nearest Routers

Li¹,

Sun²,

Hu³

et al. 2018

Security and Communication Networks

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High reliable street-level landmarks are the basis of IP geolocation, but landmark evaluation methods having been proposed cannot evaluate the street-level landmarks. Therefore, in this paper, a street-level landmark evaluation method based on nearest router is proposed. The location organization declared is regarded as an area not a point. Firstly, the declared location of preevaluated landmark is verified by IP location databases. Secondly, the preevaluated landmarks are grouped according to their nearest router. Then, the distance constraint is obtained using delay value between landmark and its nearest router by delay-distance correlation. And relation model is established among distance constraint, organization’s region radius, and distance between two landmarks. Finally, the reliability value of landmarks is calculated in each group based on relational model and binomial distribution. Landmarks evaluation experiment is taken based on 7082 preevaluated landmarks, and the results show that geolocation errors decrease obviously using evaluated landmarks. The mean error of 100 targets in Shanghai is reduced from 7.832km to 2.185km.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Street-Level Landmark Evaluation Based on Nearest Routers

Li¹,

Sun²,

Hu³

et al. 2018

Security and Communication Networks

Self Cite

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“…ey significantly improved the accuracy of landmarks based on the reduction of evaluation costs. Zhu et al [14] proposed the E-GeoTrack algorithm to vote on the city of a landmark based on the implicit information in the nearest router. However, these methods can only determine the city-level location of the landmarks.…”

Section: Introductionmentioning

confidence: 99%

Evaluator: A Multilevel Decision Approach for Web-Based Landmark Evaluation

Yin¹,

Yang²,

Liu³

et al. 2020

Security and Communication Networks

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Street-level landmarks are an important basis for street-level IP geolocation, and the web-based landmark is one of the main sources of street-level landmarks. Considering the existing street-level landmark evaluation methods having low accuracy and strict constraints, this paper analyses the causes and evaluation idea of invalid web-based candidate landmarks and proposes Evaluator, a web-based landmark evaluation approach. Evaluator adopts the idea of the decision tree to filter invalid landmarks layer by layer and comprehensively estimates the quantitative reliability of candidate landmarks with public data and services to obtain reliable landmarks. This paper proposes the domain name system (DNS) distributed query algorithm to effectively resolve all IP addresses of a domain name, which provides data support for Evaluator to filter candidate landmarks. Meanwhile, this paper also proposes a reverse verification algorithm to obtain all domain names of an IP address, which provides an important reference to calculate the reliability of a reliable landmark. In addition, gradient descent is used to assess the parameters of the reliability estimating model, which effectively improves the robustness of Evaluator. Experiments show that reliable landmarks from Evaluator reduce the geolocation error of 100 targets in Hong Kong from 7.30 km to 3.91 km, compared with the landmark verifying method (LVM), one of the latest web-based landmark evaluation methods. Moreover, Evaluator significantly improves the evaluation coverage based on the same geolocation accuracy with street-level landmark evaluation (SLE), one of the latest landmark evaluation methods.

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“…How to obtain abundant street-level landmarks in a short time is a problem that needs to be solved in street-level IP geolocation. Currently, the main methods for obtaining landmarks are databases query and Web-based mining [Guo, Liu, Shen et al (2009); Zhu, Luo, Liu et al (2015); Wang, Burgener, Flores et al (2011); Jiang, Liu and Matthews (2016)]. The landmarks acquisition based on the IP location databases query uses the databases' API interface to acquire landmarks.…”

Section: Introductionmentioning

confidence: 99%

“…Structon is a typical method based on Web mining, which brings possibility to street-level IP geolocation. After Structon, Zhu et al [Zhu, Luo, Liu et al (2015)] proposed a city-level landmark mining method based on Internet forum. Wang et al [Wang, Burgener, Flores et al (2011)] acquire organization's location and Web servers according online maps.…”

Section: Introductionmentioning

confidence: 99%

Street-Level Landmarks Acquisition Based on SVM Classifiers

Li¹,

Liu²,

Qiao³

et al. 2019

Computers, Materials &Amp; Continua

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High-density street-level reliable landmarks are one of the important foundations for street-level geolocation. However, the existing methods cannot obtain enough street-level landmarks in a short period of time. In this paper, a street-level landmarks acquisition method based on SVM (Support Vector Machine) classifiers is proposed. Firstly, the port detection results of IPs with known services are vectorized, and the vectorization results are used as an input of the SVM training. Then, the kernel function and penalty factor are adjusted for SVM classifiers training, and the optimal SVM classifiers are obtained. After that, the classifier sequence is constructed, and the IPs with unknown service are classified using the sequence. Finally, according to the domain name corresponding to the IP, the relationship between the classified server IP and organization name is established. The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially, and the median geolocation error using evaluated landmarks is reduced by about 2 km.

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An Algorithm of City-Level Landmark Mining Based on Internet Forum

Cited by 10 publications

References 9 publications

Street-Level Landmark Evaluation Based on Nearest Routers

Street-Level Landmark Evaluation Based on Nearest Routers

Evaluator: A Multilevel Decision Approach for Web-Based Landmark Evaluation

Street-Level Landmarks Acquisition Based on SVM Classifiers

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