Abstract
The progress of target network topology discovery research is slow due to the uncertainty of the target network range, and large-scale network measurement activities lead to a large accumulation of “false links” in the results, it is difficult to obtain a relatively accurate network topology. In this paper, we proposed a statistical-based target network steady-state topology discovery method. By statistical analyzing the characteristics of the measured data, we have proposed corresponding solutions to network boundary recognition, “false links” deletion and network steady-state topology construction, so that a relatively complete and accurate target network steady topology can be obtained. We also use this method to probe the HK (Hong Kong) and TW (Tai Wan) network and compare it with the data of CAIDA in the same period. Not only do the number of nodes and edges found are increased by two or three orders of magnitude, but also the number of “false links” in the results is greatly reduced.
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Acknowledgment
This work was supported by the National Key R&D Program of China (No. 2016YFB0801303, 2016QY01W0105), the National Natural Science Foundation of China (No.61309007, U1636219, 61602508, 61772549, U1736214, 61572052) and Plan for Scientific Innovation Talent of Henan Province (No. 2018JR0018).
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Yang, D., Liu, Y., Chen, J. (2019). Steady-State Topology Discovery of Target Networks Based on Statistics Method. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11635. Springer, Cham. https://doi.org/10.1007/978-3-030-24268-8_34
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DOI: https://doi.org/10.1007/978-3-030-24268-8_34
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