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  • Open Access


    Street-Level IP Geolocation Algorithm Based on Landmarks Clustering

    Fan Zhang1,2, Fenlin Liu1,2,*, Rui Xu3,4, Xiangyang Luo1,2, Shichang Ding5, Hechan Tian1,2

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3345-3361, 2021, DOI:10.32604/cmc.2021.014526

    Abstract Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays. However, this principle is often invalid in real Internet environment, which leads to unreliable geolocation results. To improve the accuracy and reliability of locating IP in real Internet, a street-level IP geolocation algorithm based on landmarks clustering is proposed. Firstly, we use the probes to measure the known landmarks to obtain their delay vectors, and cluster landmarks using them. Secondly, the landmarks are clustered again by their latitude and longitude, and the intersection of these… More >

  • Open Access


    City-Level Homogeneous Blocks Identification for IP Geolocation

    Fuxiang Yuan, Fenlin Liu, Chong Liu, Xiangyang Luo*

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1403-1417, 2020, DOI:10.32604/iasc.2020.011902

    Abstract IPs in homogeneous blocks are tightly connected and close to each other in topology and geography, which can help geolocate sensitive target IPs and maintain network security. Therefore, this manuscript proposes a city-level homogeneous blocks identification algorithm for IP geolocation. Firstly, IPs with consistent geographic location information in multiple databases and some landmarks in a specific area are obtained as targets; the /31 containing each target is used as a candidate block; vantage points are deployed to probe IPs in the candidate blocks to obtain delays and paths, and alias resolution is performed. Then, based on… More >

  • Open Access


    Street-Level Landmarks Acquisition Based on SVM Classifiers

    Ruixiang Li1,2, Yingying Liu3, Yaqiong Qiao1,2, Te Ma1,2, Bo Wang4, Xiangyang Luo1,2,*

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 591-606, 2019, DOI:10.32604/cmc.2019.05208

    Abstract High-density street-level reliable landmarks are one of the important foundations for street-level geolocation. However, the existing methods cannot obtain enough street-level landmarks in a short period of time. In this paper, a street-level landmarks acquisition method based on SVM (Support Vector Machine) classifiers is proposed. Firstly, the port detection results of IPs with known services are vectorized, and the vectorization results are used as an input of the SVM training. Then, the kernel function and penalty factor are adjusted for SVM classifiers training, and the optimal SVM classifiers are obtained. After that, the classifier sequence More >

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