Geographical Location Recognition of IP Based on Network Structure Features

Gaolei FEI; Yameng ZHANG; Zhiyu HU; Lei ZHOU; Guangmin HU

doi:10.11999/JEIT180589

Volume 41 Issue 5

Apr. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(5): 1235-1242

Gaolei FEI, Yameng ZHANG, Zhiyu HU, Lei ZHOU, Guangmin HU. Geographical Location Recognition of IP Based on Network Structure Features[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1235-1242. doi: 10.11999/JEIT180589

Citation:

Gaolei FEI, Yameng ZHANG, Zhiyu HU, Lei ZHOU, Guangmin HU. Geographical Location Recognition of IP Based on Network Structure Features[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1235-1242. doi: 10.11999/JEIT180589

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Geographical Location Recognition of IP Based on Network Structure Features

doi: 10.11999/JEIT180589

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (61301274, 61471101), The Fundamental Research Funds for the Central Universities (ZYGX2015Z008)

Received Date: 2018-06-13
Rev Recd Date: 2018-12-17

Available Online: 2019-01-07

Publish Date: 2019-05-01

Abstract

Abstract

The existing IP location technology determines the location of IP by querying IP to register information databases or using time-delay information. In fact, due to the influence of various factors, most of the IP in the network can not get accurate and reasonable positioning results. For this reason, a region recognition method of IP is proposed based on network structure features. This method obtains the network topology information between the two nodes by sending the Traceroute detection packet from the detection nodes to the IPs that need to be located Comparing the network structure features between the nodes to be located and the known geographical nodes determines where the nodes located. The actual test shows that this method can achieve better results.
- Network structure features,
- Feature similarity,
- Regional identification of IP,
- Network measurement

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References(16)

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