Improving IP Geolocation using Query Logs

Dan, Ovidiu; Parikh, Vaibhav; Davison, Brian D.

doi:10.1145/2835776.2835820

Cited by 23 publications

(9 citation statements)

References 19 publications

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“…The generic results lead to nationwide websites where the user has to requery for more specific restaurants in their area, while the personalized results directly list restaurants tailored to a specific location. Previous work has shown that personalizing results to a user's location leads to increased user satisfaction and conversely that missing location information leads to user dissatisfaction [4,14,37]. IP geolocation databases are also used in many other applications, including:…”

Section: Introductionmentioning

confidence: 99%

“…Related work has shown that while they have high coverage, commercial databases are sometimes inaccurate or are missing location information for some IP ranges [17,26,47,54]. Our own past work has demonstrated that the city-level accuracy of geolocation databases is less than 70% in 10 major countries [14]. Furthermore, the methods they use to derive their databases from these raw data sources are proprietary and therefore impossible to properly reproduce and expand upon in an academic settings.…”

Section: Introductionmentioning

confidence: 99%

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IP Geolocation through Reverse DNS

Dan

Parikh

Davison

2021

ACM Trans. Internet Technol.

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IP Geolocation databases are widely used in online services to map end-user IP addresses to their geographical location. However, they use proprietary geolocation methods, and in some cases they have poor accuracy. We propose a systematic approach to use reverse DNS hostnames for geolocating IP addresses, with a focus on end-user IP addresses as opposed to router IPs. Our method is designed to be combined with other geolocation data sources. We cast the task as a machine learning problem where, for a given hostname, we first generate a list of potential location candidates, and then we classify each hostname and candidate pair using a binary classifier to determine which location candidates are plausible. Finally, we rank the remaining candidates by confidence (class probability) and break ties by population count. We evaluate our approach against three state-of-the-art academic baselines and two state-of-the-art commercial IP geolocation databases. We show that our work significantly outperforms the academic baselines and is complementary and competitive with commercial databases. To aid reproducibility, we open source our entire approach and make it available to the academic community.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IP Geolocation through Reverse DNS

Dan

Parikh

Davison

2021

ACM Trans. Internet Technol.

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“…The results showed that these databases are unsatisfactory, and there is considerable room for improvement. Reference [7] evaluated 3 popular databases and noted that the average accuracy at the city level of these databases is less than 70%.…”

Section: Introductionmentioning

confidence: 99%

Street-Level Geolocation Based on Router Multilevel Partitioning

Zhao

Xu²,

et al. 2019

IEEE Access

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The high-precision geolocation of Internet hosts plays an important role in many applications, such as online advertising and deception detection. The existing typical high-precision geolocation algorithms usually utilize single-hop or relative delay to geolocate an Internet host at street-level granularity. However, it is difficult to accurately measure the single-hop or relative delay within a city. This challenge sometimes results in large geolocation errors. To solve this problem, a street-level geolocation algorithm based on router multilevel partitioning is proposed. Unlike existing typical algorithms, the proposed algorithm makes a credible hypothesis that each router has a relatively stable service object for a period of time. By analyzing the connection between routers and landmarks, the possible geographic service ranges of routers are inferred from the geographic distribution of landmarks. Then, distance constraints arising from routers' service ranges are formed to estimate the geographic location of the target IP. Theoretical analysis of the geolocation error shows that the maximum and average errors of the proposed algorithm are less than those of existing typical algorithms. The proposed algorithm is evaluated by geolocating a total of 12,152 target IP addresses located in four cities in different regions. The experimental results show that, compared with the existing typical street-level geolocation algorithms SLG and NC-Geo, the average median error of the proposed algorithm decreases from 4.735 km and 3.776 km to 3.25 km, representing error reductions of approximately 31.36% and 13.96%, respectively. INDEX TERMS IP geolocation, delay-distance correlation, multilevel partitioning of routers, service range calculation.

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“…In [20,21], the authors analysed the distribution characteristics of IP address blocks of different geographic granularity in mainland China, compared the data consistency between multiple IP location database, verified the accuracy of the IP location database using a small number of existing landmarks, and improved the accuracy of the database by clustering IP blocks at different locations. For improving the accuracy of database, location data collected from search engine logs [22], IP allocation strategy of ISPs, urban areas, and population [23] were introduced into the database evaluation methods. Using those methods improve the accuracy at city level, but there are inadequacies in evaluating fine-grained (e.g., street level) locations.…”

Section: Introductionmentioning

confidence: 99%

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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Improving IP Geolocation using Query Logs

Cited by 23 publications

References 19 publications

IP Geolocation through Reverse DNS

IP Geolocation through Reverse DNS

Street-Level Geolocation Based on Router Multilevel Partitioning

Street-Level Landmark Evaluation Based on Nearest Routers

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