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http://dx.doi.org/10.3837/tiis.2021.08.004

A City-Level Boundary Nodes Identification Algorithm Based on Bidirectional Approaching  

Tao, Zhiyuan (State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou)
Liu, Fenlin (State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou)
Liu, Yan (State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou)
Luo, Xiangyang (State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.8, 2021 , pp. 2764-2782 More about this Journal
Abstract
Existing city-level boundary nodes identification methods need to locate all IP addresses on the path to differentiate which IP is the boundary node. However, these methods are susceptible to time-delay, the accuracy of location information and other factors, and the resource consumption of locating all IPes is tremendous. To improve the recognition rate and reduce the locating cost, this paper proposes an algorithm for city-level boundary node identification based on bidirectional approaching. Different from the existing methods based on time-delay information and location results, the proposed algorithm uses topological analysis to construct a set of candidate boundary nodes and then identifies the boundary nodes. The proposed algorithm can identify the boundary of the target city network without high-precision location information and dramatically reduces resource consumption compared with the traditional algorithm. Meanwhile, it can label some errors in the existing IP address database. Based on 45,182,326 measurement results from Zhengzhou, Chengdu and Hangzhou in China and New York, Los Angeles and Dallas in the United States, the experimental results show that: The algorithm can accurately identify the city boundary nodes using only 20.33% location resources, and more than 80.29% of the boundary nodes can be mined with a precision of more than 70.73%.
Keywords
Internet measurement; Network topology; Boundary node identification; Bidirectional approaching;
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1 F. Zhao, R. Xu, R. X. Li, M. Zhu, and X. Y. Luo, "Street-level geolocation based on router multilevel partitioning," IEEE Access, vol. 7, pp. 59237-59248, 2019.   DOI
2 J. P. Liu, X. C. Kang, C. Dong, and F. H. Zhang, "Simulation of real-time path planning for large-scale transportation network using parallel computation," Intelligent Automation & Soft Computing, vol. 25, no.1, pp. 65-77, Jan., 2019.
3 Y. Tian, R. Dey, Y. Liu, and K. W. Ross, "Topology mapping and geolocating for China's Internet," IEEE Transactions on Parallel and Distributed Systems, vol. 24, pp. 1908-1917, Sept. 2013.   DOI
4 M. Luckie, "Scamper: a scalable and extensible packet prober for active measurement of the internet," in Proc. of ACM SIGCOMM conference on Internet measurement, Melbourne, Australia, pp. 239-245, 2010.
5 G. Ciavarrini, M. S. Greco, and A. Vecchio, "Geolocation of internet hosts: accuracy limits through Cramer-Rao lower bound," Computer Networks, vol. 135, pp. 70-80, Apr, 2018.   DOI
6 J. Chen, Y. Luo and R. Du, "The impact of privacy seal on users' perception in network transactions," Computer Systems Science and Engineering, vol. 35, no.3, pp. 199-206, May, 2020.   DOI
7 L. Matthew, D. Amogh, H. Bradley, C. David, and C. Kc, "Bdrmap: inference of borders between IP networks," in Proc. of Internet Measurement Conference, Santa Monica, CA, USA, pp.381-396, 2016.
8 B. Donnet, P. Raoult, T. Friedman, and M. Crovella, "Deployment of an algorithm for large-scale topology discovery," IEEE Journal on Selected Areas in Communications, vol. 24, no. 12, pp. 2210-2220, Dec, 2006.   DOI
9 F. X. Yuan, F. L. Liu, R. Xu, Y. Liu, and X. Y. Luo, "Network topology boundary routing IP identification for IP geolocation," in Proc. of International Conference on Artificial Intelligence and Security, Hohhot, China, pp. 534-544, 2020.
10 J. N. Chen, F. L. Liu, Y. F. Shi, and X. Y. Luo, "Towards IP location estimation using the nearest common router," Journal of Internet Technology, vol. 19, no. 7, pp. 2097-2110, 2018.
11 V. Giotsas, G. Smaragdakis, B. Huffaker, M. J. Luckie, and K. C. Claffy, "Mapping peering interconnections to a facility," in Proc. of the ACM Conference on Emerging Networking Experiments and Technologies, Heidelberg, Germany, pp. 1-13, 2015.
12 A. Marder, and J. M. Smith, "MAP-IT: multipass accurate passive inferences from traceroute," in Proc. of the ACM SIGCOMM Internet Measurement Conference, Santa Monica, CA, USA, pp. 397-411, 2016.
13 S. Kaur and V. K. Joshi, "Hybrid soft computing technique based trust evaluation protocol for wireless sensor networks," Intelligent Automation & Soft Computing, vol. 26, no.2, pp. 217-226, Jan, 2020.
14 G. Swathi, "A frame work for categorise the innumerable vulnerable nodes in mobile adhoc network," Computer Systems Science and Engineering, vol. 35, no.5, pp. 335-345, Jan, 2020.   DOI
15 M. Luckie, B. Huffaker, A. Dhamdhere, V. Giotsas, and K. Claffy, "AS relationships, customer cones, and validation," in Proc. of the ACM SIGCOMM Internet Measurement Conference, Barcelona, Spain, pp. 243-256, 2013.
16 Y. W, D. Burgener, F. Marcel, K. Aleksandar; and C. Huang, "Towards street-level client-independent IP geolocation," in Proc. of USENIX Symposium on Networked Systems Design and Implementation, Boston, MA, USA, pp. 365-379, 2011.
17 A. Marder, M. Luckie A. Dhamdhere, B. Huffaker, and J. M. Smith, "Pushing the Boundaries with bdrmapIT: Mapping router ownership at Internet scale," in Proc. of the ACM SIGCOMM Internet Measurement Conference, Boston, MA, USA, pp. 56-69, 2018.
18 S. Q. Liu, F. L. Liu, F. Zhao, L. X. Chai, and X. Y. Luo, "IP city-level geolocation based on the pop-level network topology analysis," in Proc. of International Conference on Information Communication and Management, Hatfield, UK, pp. 109-114, 2016.
19 F. Zhao, X. Y. Luo, Y. Gan, X. D. Zu, J. N. Chen, and F. L. Liu, "IP geolocation based on identification routers and local delay distribution similarity," Concurrency Computation, vol. 31, no. 22, pp. 1-15, Nov., 2018.