Abstract
IP geolocation technology can be used to obtain the real-time locations of target Internet hosts especially mobile computers, which can help law enforcement to quickly get the criminal evidence or arrest criminals. Among existing numerous geolocation methods, SLG (Street-Level Geolocation) method can achieve geolocation result with relative higher precision for a target host. However, the geolocation accuracy will be significantly reduced once the common routers that play an important role in geolocation are anonymous, which often happens in paths detection. To solve this problem, this paper proposes an IP geolocation algorithm base on local delay distribution similarity. Firstly, the target’s location at city-level granularity is obtained based on traditional SLG method. Secondly, the landmarks connected with the target by common routers are found out by topology analysis. The target’s local delay between the nearest common router and the target is gained by multi-measurement and calculation, so do the landmarks’. Thirdly, their local delay distribution is obtained by statistical analysis. Lastly, the landmark whose local delay distribution is the most similar with the target’s is selected as the estimated location of the target. Experimental results show that the proposed algorithm obviously improves the geolocation accuracy compared with traditional SLG when the common routers are anonymous.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Taylor, J., Devlin, J., Curran, K.: Bringing location to ip addresses with ip geolocation. J. Emerging Technol. Web Intell. 4(3), 273–277 (2012)
Whois Homepage. https://www.whois.com/. Accessed 24 June 2017
Akamai Homepage. https://www.akamai.com/. Accessed 24 June 2017
Maxmind Homepage. https://www.maxmind.com/zh/home. Accessed 24 June 2017
Quova Homepage. https://www.neustar.biz/risk/compliance-solutions/ip-intelligence. Accessed 24 June 2017
IP2location Homepage, http://www.ip2location.com/. Accessed 24 June 2017
CQ Counter Homepage, http://cqcounter.com/. Accessed 24 June 2017
Shavitt, Y., Zilberman, N.: A geolocation databases study. IEEE J. Sel. Areas Commun. 29(10), 2044–2056 (2011)
Poese, I., Uhlig, S., Kaafar, M.A., Donnet, B., Gueye, B.: IP geolocation databases: unreliable? ACM SIGCOMM Comput. Commun. Rev. 41(2), 53–56 (2011)
Lee, Y., Park, H., Lee, Y.: IP Geolocation with a crowd-sourcing broadband performance tool. ACM SIGCOMM Comput. Commun. Rev. 46(1), 12–20 (2016)
Padmanabhan, V.N., Subramanian, L.: An investigation of geographic mapping techniques for internet hosts. ACM SIGCOMM Comput. Commun. Rev. 31(4), 173–185 (2001)
Gueye, B., Ziviani, A., Crovella, M., Fdida, S.: Constraint-based geolocation of internet hosts. IEEE/ACM Trans. Netw. 14(6), 1219–1232 (2006)
Dong, Z., Perera, R.D.W., Chandramouli, R., Subbalakshmi, K.P.: Network measurement based modeling and optimization for IP geolocation. Comput. Netw. 56(1), 85–98 (2012)
Laki, S., Mátray, P., Hága, P., Csabai, I., Vattay, G.: A model based approach for improving router geolocation. Comput. Netw. 54(9), 1490–1501 (2010)
Arif, M.J., Karunasekera, S., Kulkarni, S.: GeoWeight: internet host geolocation based on a probability model for latency measurements. In: Proceeding of the ACSC 2010 33rd Australasian Conference on Computer Science, pp. 89–98 (2010)
Eriksson, B., Barford, P., Sommers, J., Nowak, R.: A learning-based approach for IP geolocation. In: Krishnamurthy, A., Plattner, B. (eds.) PAM 2010. LNCS, vol. 6032, pp. 171–180. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12334-4_18
Maziku, H., Shetty, S., Han, K., Rogers, T.: Enhancing the classification accuracy of IP geolocation. In: The 31st International Proceedings on Military Communications, pp. 1–6. IEEE, Florida (2012)
Arif, M.J., Karunasekera, S., Kulkarni, S., Gunatilaka, A., Ristic, B.: Internet host geolocation using maximum likelihood estimation technique. In: The 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 422–429. IEEE, Perth (2010)
Laki, S., Mátray, P., Hága, P., Sebök, T., Csabai, I., Vattay, G.: Spotter: a model based active geolocation service. In: The 30th IEEE International Conference on Computer Communication, pp. 3173–3181. IEEE, Shanghai (2011)
Jiang, H., Liu, Y., Matthews, J.N.: IP geolocation estimation using neural networks with stable landmarks. In: The 35th IEEE International Workshops on Computer Communications, pp. 170–175. IEEE, San Francisco (2016)
Katz-Bassett, E., John, J.P., Krishnamurthy, A., Wetherall, D.: Towards IP geolocation using delay and topology measurements. In: the 6th ACM SIGCOMM Conference on Internet measurement, pp. 71–84. ACM, Berlin (2006)
Wong, B., Stoyanov, I., Sirer, E.G.: Octant: a comprehensive framework for the geolocalization of Internet hosts. In: The 4th USENIX Symposium on Networked Systems Design & Implementation, pp. 313–326. USENIX Association, Berkeley (2007)
Wang, Y., Burgener, D., Flores, M., Kuzmanovic, A., Huang, C.: Towards street-level client-independent IP geolocation. In: The 8th USENIX Conference on Networked Systems Design and Implementation, pp. 1–14. USENIX Association, Boston (2011)
Tian, Y., Dey, R., Liu, Y., Ross, K.W.: China’s internet: Topology mapping and geolocating. In: The 31th IEEE International Conference on Computer Communication, pp. 2531–2535. IEEE, Florida (2012)
Siqi, L., Fenlin, L., Fan, Z., Lixiang, C., Xiangyang, L.: IP city-level geolocation based on the PoP-level network topology analysis. In: The 6th International Conference on Information Communication and Management, pp. 109–114. IEEE, Hertfordshire (2016)
Chuanxiong, G., Wenchao, S., Helen, J.W., Qing, Y., Yongguang, Z.: Mining the web and the internet for accurate ip address geolocations. In: The 28th IEEE International Conference on Computer Communication, pp. 2841–2845. IEEE, Rio de Janeiro (2009)
Hao, L., Yaoxue, Z., Yuezhi, Z., Di, Z., Xiaoming, F., Ramakrishnan, K.K.: Mining checkins from location-sharing services for client-independent IP geolocation. In: The 33rd IEEE International Conference on Computer Communication, pp. 619–627. IEEE, Toronto (2014)
Dan, O., Parikh, V., Davison, B.D.: Improving IP geolocation using query logs. In: The 9th ACM International Conference on Web Search and Data Mining, pp. 347–356. ACM, Shanghai (2016)
Randolph, B.: IP Geolocation in Metropolitan Area Networks. University of Maryland, College Park (2008)
Prieditis, A., Chen, G.: Mapping the internet: geolocating routers by using machine learning. In: The 4th International Conference on Computing for Geospatial Research and Application, pp. 101–105. ACM, San Jose (2013)
Acknowledgment
The work presented in this paper is supported by the National Natural Science Foundation of China (No. U1636219, 61379151, 61401512, 61572052), the National Key R&D Program of China (No. 2016YFB0801303, 2016QY01W0105) and the Key Technologies R&D Program of Henan Province (No. 162102210032).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhao, F., Luo, X., Gan, Y., Zu, S., Liu, F. (2017). IP Geolocation Base on Local Delay Distribution Similarity. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-69471-9_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69470-2
Online ISBN: 978-3-319-69471-9
eBook Packages: Computer ScienceComputer Science (R0)