The geolocation covers 4.3 billion IP, including all network equipment, such as PC, mobile phone, etc.
Range of ultra-high precision geolocation can reach 50m to 5km, county-level geolocate to county center.
Geolocation result returns continent, country, province, city, county, longitude and latitude, zip code, operator and other information.
Ultra-high precision API, weekly update；district level API, daily update.
It supports 1000 times/second concurrency, and returns the geolocation result in 10 ms.
Aiming at the precision requirements of IP geolocation in two different usage scene of business and security, Aivan IP geolocation is divided into commercial version and police version.
|Application Scenarios||IP Number||Geolocation Accuracy||The Most High Precision Range||Average Geolocation Range|
|Police Version||Commercial version||Police Version||Commercial version|
|Company Class（Company, Special Export and Organization）||About 27 million||Street-Level||10 m||50 m||50m||300m|
|Data Center Class（IDC and CDN）||About 37 million||Street-Level||10 m||100 m||200 m||1 km|
|University Class||About 18||Street-Level||10 m||100 m||300 m||1 km|
|Home Broadband Class||About 120 million||Street-Level||50 m||1 km||2 km||5 km|
|Mobile Network Class（3G、4G and WLAN）||About 5.2 million||Province-level||-||-||-||-|
|Others||About 170,000||Street-Level||10 m||1 km||1 km||2 km|
IP geolocation uses big data mining and large-scale network detection technology to collect and process the basic information of IP addresses and network topology data, combines IP scene and network attributes and other factors to complete high-precision IP geographic location through comprehensive analysis of algorithms.
This product is based on the paper Towards Street-Level Client-Independent IP Geolocation (referred to as SLG, published in NSDI 2010) jointly published by Dr. Wang Yong, founder of Aiwen, Microsoft Research and Northwestern University. This paper has been cited 146 times. Partial reference is as follows.
|Quantitatively assessing and visualising industrial system attack surfaces||Eireann P. Leverett||University of Cambridge|
|I know where you are and what you are sharing: exploiting P2P communications to invade users' privacy||Stevens Le Blond, Chao Zhang, Arnaud Legout, Keith Ross, Walid Dabbous||University of Kaiserslautern|
|Pingin'in the rain||Aaron Schulman, Neil Spring||University of Maryland|
|Regional variation in Chinese internet filtering||Joss Wright||University of Oxford|
|Towards geolocation of millions of IP addresses||Zi Hu, John Heidemann, Yuri Pradkin||University of Southern California|
|Fine-Grained Censorship Mapping: Information Sources, Legality and Ethics.||Joss Wright||University of Oxford|
|Topology mapping and geolocating for China's Internet||Ye Tian, Ratan Dey, Yong Liu, Keith W. Ross||University of Science and Technology of China，New York University|
|China's Internet: Topology mapping and geolocating||Ye Tian, Ratan Dey, Yong Liu, Keith W. Ross||University of Science and Technology of China，New York University|
|Posit: a lightweight approach for IP geolocation||Brian Eriksson, Paul Barford, Bruce Maggs, Robert Nowak||University of Wisconsin System，Duke University|
|IP-Geolocation Mapping for Moderately Connected Internet Regions||Dan Li, Jiong Chen, Chuanxiong Guo, Yunxin Liu, Jinyu Zhang, Zhili Zhang, Yongguang Zhang||Tsinghua University，Peking University，MSRA，University of Minnesota System|
|DRoP: DNS-based router positioning||Bradley Huffaker, Marina Fomenkov, kc claffy||University of California, San Diego|
|How others compromise your location privacy: The case of shared public IPs at hotspots||Nevena Vratonjic, Kévin Huguenin, Vincent Bindschaedler, Jean-Pierre Hubaux||Swiss Federal Institute of Technology Zurich，University of Illinois|
|Layer 1-informed internet topology measurement||Ramakrishnan Durairajan, Joel Sommers, Paul Barford||University of Wisconsin System|
|Mining checkins from location-sharing services for client-independent ip geolocation||Hao Liu, Yaoxue Zhang, Yuezhi Zhou, Di Zhang, Xiaoming Fu, K.K. Ramakrishnan||Tsinghua University，University of Gottingen，Rutgers|
|HawkEyes: An advanced IP Geolocation approach: IP Geolocation using semantic and measurement based techniques||Art Dahnert||Overwatch Systems Ltd.|
|Understanding geolocation accuracy using network geometry||Brian Eriksson, Mark Crovella||Palo Alto Institute of Technology，Boston University|
|Measuring the relationships between internet geography and rtt||Raul Landa, Richard G. Clegg, Joao Taveira Araujo, Eleni Mykoniati, David Griffin, Miguel Rio||University College London|
|Posit: An adaptive framework for lightweight ip geolocation||Brian Eriksson, Paul Barford , Bruce Maggs, Robert Nowak||Boston University，University of Wisconsin System，Duke University|
|Violating consumer anonymity: Geo-locating nodes in named data networking||Alberto Compagno, Mauro Conti, Paolo Gasti, Luigi Vincenzo Mancini, Gene Tsudik||At the university of Rome，University of Padua，New York Institute of Technology，University of California|
|IXmaps—Tracking your personal data through the NSA's warrantless wiretapping sites||Andrew Clement||University of Toronto|
We determine the source of network attack IP by geolocating IP, and conduct network security defense. For example, the government department determines the location of the network attack by geolocating the geographic location of the network attack IP.
Internet financial credit investigation, anti-fraud and location verification. For example, the insurance company determines whether the customer appears in the usual place of residence by determining the location of the customer's IP, thereby reducing credit risk.
Online advertising based on IP location improves the efficiency of advertising. For example, advertisers put corresponding ads for IPs in different geographic locations and Increase the likelihood of customer purchasing
We provide ultra-high precision geographic location data analysis latitude and location verification integration services. For example, Internet companies determine the user's living habits by analyzing the location information of the IP used by customers at different points in time.