Abstract
IP geolocation technology can estimate or determine the geographical location of terminal device through IP address, which is an important basis for location-based network services. The existing IP geolocation methods based on network measurement cannot accurately obtain the network environment when the target does not respond to the detection message, so it is difficult to achieve high-precision geolocation. To solve this problem, a reachable landmark grouping based IP geolocation method for unreachable target (RLGBG) is proposed in this manuscript. RLGBG first detects landmark data extensively, filters, evaluates and retains reliable and accessible landmarks. Secondly, RLGBG counts the topological relationship and geographical distribution among the reachable landmarks, groups the landmarks according to the common router, and divides the landmarks into different groups after checking by geographical location. Then, the continuous IP segments are divided into IP blocks according to landmark groups, and the location range of each IP block is calculated by the landmarks within each group. Finally, RLGBG counts the IP blocks containing the target, merges those IP blocks that are geographically coincident and topologically related, and estimates the target’s location by calculating the landmark center within the IP blocks. The geolocation experiments based on four cities in China show that RLGBG can effectively estimate the location of unreachable IP targets, and its accuracy and coverage are higher than the existing typical method and IP location databases.
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References
Niu, B., Li, Q., Zhu, X., Cao, G., Li, H.: Enhancing privacy through caching in location-based services. In: 2015 IEEE Conference on Computer Communications (INFOCOM), 1017–1025. IEEE (2015)
Zheng, X., Cai, Z., Li, J., Gao, H.: Location-privacy-aware review publication mechanism for local business service systems. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications, 1–9. IEEE (2017)
Burch, H., Cheswick, B.: Mapping the internet. Computer 32(4), 97–98 (1999)
Chen, J., Liu, F., Zhao, F., Zhu, G., Ding, S.: A SC-Vivaldi network coordinate system based method for IP geolocation. J. Internet Technol. 17(1), 119–127 (2016)
Liu, C., Luo, X., Yuan, F., Liu, F.: RNBG: a ranking nodes based IP geolocation method. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 80–84. IEEE (2020)
Zhang, F., Liu, F., Xu, R., Luo, X., Ding, S., Tian, H.: Street-level IP geolocation algorithm based on landmarks clustering. Comput. Mater. Cont. 66(3), 3345–3361 (2021)
Ding, S., Luo, X., Wang, J., Fu, X.: GNN-Geo: A Graph Neural Network-Based Fine-grained IP Geolocation Framework. arXiv preprint arXiv:2112.10767 (2021)
Li, D., Chen, J., Guo, C., Liu, Y., Zhang, J., Zhang, Z., Zhang, Y.: IP geolocation mapping for moderately connected internet regions. IEEE Trans. Parallel Distrib. Syst. 24(2), 381–391 (2012)
Gueye, B., Ziviani, A., Crovella, M., Fdida, S.: Constraint-based geolocation of internet hosts. IEEE/ACM Trans. Netw. 14(6), 1219–1232 (2006)
Laki, S., Mátray, P., Hága, P., Sebők, T., Csabai, I., Vattay, G.: Spotter: a model based active geolocation service. In: Proceedings of IEEE INFOCOM, pp. 3173–3181 (2011)
Ciavarrini, G., Greco, M.S., Vecchio, A.: Geolocation of Internet hosts: accuracy limits through Cramér–Rao lower bound. Elsevier Comput. Netw. 135, 70–80 (2018)
Katz-Bassett, E., John, J.P., Krishnamurthy, A., Wetherall, D., Anderson, T., Chawathe, Y.: Towards IP geolocation using delay and topology measurements. In: Proceedings of ACM IMC, pp. 71–84 (2006)
Wang, Y., Burgener, D., Flores, M., Kuzmanovic, A., Huang, C.: Towards street-level client-independent IP geolocation. In: Proceedings of USENIX NSDI, p. 27 (2011)
Zu, S., Luo, X., Liu, S., Liu, Y., Liu, F.: City-level IP geolocation algorithm based on pop network topology. IEEE Access 6, 64867–64875 (2018)
Zhao, F., Xu, R., Li, R., Zhu, M., Luo, X.: Street-level geolocation based on router multilevel partitioning. IEEE Access 2019(7), 59237–59248 (2019)
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)
Li, D., Chen, J., Guo, C., Liu, Y., Zhang, J., Zhang, Z., Zhang, Y.: IP-geolocation mapping for moderately connected internet regions. IEEE Trans. Parallel Distrib. Syst. 24, 381–391 (2012)
Ma, T., Liu, F., Luo, X., Yin, M., Li, R.: An algorithm of street-level landmark obtaining based on yellow pages. J. Internet Technol. 20, 1415–1428 (2019)
Li, R., Sun, Y., Hu, J., Ma, T., Luo, X.: Street-level landmark evaluation based on nearest routers. Secur. Commun. Netw. (2018)
Luckie, M.: Scamper: a scalable and extensible packet prober for active measurement of the internet. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 239–245 (2010)
Augustin, B., Cuvellier, X., Orgogozo, B., Viger, F., Friedman, T., Latapy, M., Magnien, C., Teixeira, R.: Avoiding traceroute anomalies with Paris traceroute. In: Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement, pp. 153–158 (2006)
Acknowledgements
This work was supported by the Henan Key Laboratory of Cyberspace Situation Awareness.
Funding Statement
This work was supported by the National Key R&D Program of China (Grant No. 2022YFB3102904), the National Natural Science Foundation of China (Grant No. U1804263, 62172435), the Zhongyuan Science and Technology Innovation Leading Talent Project of China (Grant No. 214200510019), and Key Research, Development and Promotion Project on Tackling Key Scientific Problems of Henan Province (No. 232102211052).
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Zu, S., Liu, C., Zhang, F., Ding, S. (2024). RLGBG: A Reachable Landmark Grouping Based IP Geolocation Method for Unreachable Target. In: Li, S. (eds) Computational and Experimental Simulations in Engineering. ICCES 2023. Mechanisms and Machine Science, vol 145. Springer, Cham. https://doi.org/10.1007/978-3-031-42987-3_46
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DOI: https://doi.org/10.1007/978-3-031-42987-3_46
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