City-Level IP Geolocation Method Based on Network Node Clustering

被引:0
|
作者
Li M. [1 ,2 ]
Luo X. [1 ,2 ]
Chai L. [1 ,2 ]
Yuan F. [1 ,2 ]
Gan Y. [3 ]
机构
[1] Zhengzhou Information Science and Technology Institute, Zhengzhou
[2] State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou Information Science and Technology Institute, Zhengzhou
[3] School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou
基金
中国国家自然科学基金;
关键词
City-level geolocation; Community detection; IP geolocation; Modularity; Network topology clustering;
D O I
10.7544/issn1000-1239.2019.20170473
中图分类号
学科分类号
摘要
Existing city-level target IP geolocation method based on network topology heuristic clustering (HC-Based method) clusters IP nodes by simple voting rules, which is liable to cause a lot of errors in geolocation results. This paper presents a city-level IP geolocation method based on network node clustering, referred to as the NNC method. This method firstly uses the principle that the same network community locates in the same metropolitan area network. Considering the characteristics of the module that can accurately measure the strength of the network community structure, the network topology is clustered based on the modular optimization, and the network community with the highest module degree is obtained. Then the IP geography database voting rules is used to determine the location of the network community. Finally, depending on the network community where the target IP is located in, the city where the target IP is located in can be determined. Experimental results of 15 000 Internet IP nodes in five provinces (Henan, Shandong, Shaanxi, Guangdong and Zhejiang) of China show that compared with HC-Based method, the proposed method can significantly improve the accuracy and recall rate of the target IP, and reduce the effect of the inaccurate landmarks on the location results. © 2019, Science Press. All right reserved.
引用
收藏
页码:467 / 479
页数:12
相关论文
共 27 条
  • [1] Taylor J., Devlin J., Curran K., Bringing location to IP addresses with IP geolocation, Journal of Emerging Technologies in Web Intelligence, 4, 3, pp. 273-277, (2012)
  • [2] Wang Z., Feng J., Xing C., Et al., Research on the IP geolocation technology, Journal of Software, 25, 7, pp. 1527-1540, (2014)
  • [3] Jiang H., Liu Y., Matthews J.N., IP geolocation estimation using neural networks with stable landmarks, Proc of the 36th IEEE Conf on Computer Communications Workshops, pp. 170-175, (2016)
  • [4] Lee Y., Park H., Lee Y., IP geolocation with a crowd-sourcing broadband performance tool, ACM SIGCOMM Computer Communication Review, 46, 1, pp. 12-20, (2016)
  • [5] Liu H., Zhang Y., Zhou Y., Et al., Mining checkins from location-sharing services for client-independent ip geolocation, Proc of the 14th IEEE INFOCOM, pp. 619-627, (2014)
  • [6] Weber I., Zagheni E., Studying inter-national mobility through IP geolocation, Proc of the 6th ACM Int Conf on Web Search and Data Mining, pp. 265-274, (2013)
  • [7] Li D., Chen J., Guo C., Et al., IP-geolocation mapping for moderately connected internet regions, IEEE Transactions on Parallel and Distributed Systems, 24, 2, pp. 381-391, (2013)
  • [8] Gill P., Ganjali Y., Wong B., Et al., Dude, where's that IP?: Circumventing measurement-based IP geolocation, Proc of the 19th USENIX Conf on Security, pp. 16-22, (2010)
  • [9] Zhu G., Luo X., Liu F., Et al., City-level geolocation algorithm of network entities based on landmark clustering, Proc of the 18th Int Conf on Advanced Communication Technology, pp. 306-309, (2016)
  • [10] Mao W., IP138