Abstract
IP geolocation is essential for the territorial analysis of sensitive network entities, location-based services (LBS) and network fraud detection. It has important theoretical significance and application value. Measurement-based IP geolocation is a hot research topic. However, the existing IP geolocation algorithms cannot effectively utilize the distance characteristics of the delay, and the nodes’ connection relation, resulting in high geolocation error. It is challenging to obtain the mapping between delay, nodes’ connection relation, and geographical location. Based on the idea of network representation learning, we propose a representation learning model for IP nodes (IP2vec for short) and apply it to street-level IP geolocation. IP2vec model vectorizes nodes according to the connection relation and delay between nodes so that the IP vectors can reflect the distance and topological proximity between IP nodes. The steps of the street-level IP geolocation algorithm based on IP2vec model are as follows: Firstly, we measure landmarks and target IP to obtain delay and path information to construct the network topology. Secondly, we use the IP2vec model to obtain the IP vectors from the network topology. Thirdly, we train a neural network to fit the mapping relation between vectors and locations of landmarks. Finally, the vector of target IP is fed into the neural network to obtain the geographical location of target IP. The algorithm can accurately infer geographical locations of target IPs based on delay and topological proximity embedded in the IP vectors. The cross-validation experimental results on 10023 target IPs in New York, Beijing, Hong Kong, and Zhengzhou demonstrate that the proposed algorithm can achieve street-level geolocation. Compared with the existing algorithms such as Hop-Hot, IP-geolocater and SLG, the mean geolocation error of the proposed algorithm is reduced by 33%, 39%, and 51%, respectively.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. U1804263, U1736214, 62172435) and the Zhongyuan Science and Technology Innovation Leading Talent Project (No. 214200510019).
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Fan Zhang received his BS degree from Xiangtan University, China in 2017 and his MS degree from the State Key Laboratory of Mathematical Engineering and Advanced Computing, China in 2020. He has been with the State Key Laboratory of Mathematical Engineering and Advanced Computing since July 2017. His research interest includes network security, network measurement, and network geolocation. He received the support of the National Natural Science Foundation of China and the Basic and Frontier Technology Research Program of Henan Province.
Meijuan Yin was conferred a PhD in computer application by State Key Laboratory of Mathematical Engineering and Advanced Computing, China in 2012. She is now an associate professor of the laboratory. Her current research interests include data mining, social network analysis, and information security.
Fenlin Liu received the BS degree from the Zhengzhou Science and Technology Institute, China in 1986, the MS degree from the Harbin Institute of Technology, China in 1992, and the PhD degree from Northeast University, China in 1998. He is currently a professor with the Zhengzhou Science and Technology Institute, China. He has authored or co-authored more than 90 refereed international journal and conference papers. His research interests include network topology and network geolocation.
Xiangyang Luo is currently a professor at Zhengzhou Science and Technology Institute and the State Key Laboratory of Mathematical Engineering and Advanced Computing, China. His research interests lie in multimedia security and cyberspace surveying and mapping. He is the author or co-author of more than 100 refereed international journal and conference papers. He has obtained the support of the National Natural Science Foundation of China and the National Key R&D Program of China.
Shuodi Zu received his BS and MS from the State Key Laboratory of Mathematical Engineering and Advanced Computing, China in 2016. He has been with the State Key Laboratory of Mathematical Engineering and Advanced Computing since July 2012. His research interest includes network security, network measurement and network geolocation. He received the support of the National Natural Science Foundation of China and the Basic and Frontier Technology Research Program of Henan Province.
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Zhang, F., Yin, M., Liu, F. et al. IP2vec: an IP node representation model for IP geolocation. Front. Comput. Sci. 18, 186506 (2024). https://doi.org/10.1007/s11704-023-2616-9
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DOI: https://doi.org/10.1007/s11704-023-2616-9