# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 1 Introduction  
As we know, IP addresses have a dual semantic meaning, representing both identity and location [1]. IP geolocation is a technology that aims to provide high-precision geographic information, such as the country, region, latitude, longitude, and time zone of the host [2]. Actually, IP geolocation is important to Internet users. It has accommodated many location-based services, personalized recommendations, Whois, DNS, public databases, etc. Furthermore, it is an important tool for back-tracing and tracking in the field of network security [3]. Existing IP geolocation algorithms are mainly divided into two categories: (1) database-query-based IP geolocation algorithms, as early research focused on the records provided by public databases including WHOIS and DNS, but the database is poorly updated and maintained, making its geolocation performance degraded; existing research is focusing more on improving geographic position performance by enhancing database data reliability [4]; (2) the network-measurement-based IP geolocation algorithm collects the network measurement data between the probe and the target IP, constructs the measurement data geographic distance mapping with different methods, generates distance constraints for the target IP, and estimates the geographic location information of the target IP [5]; in addition, it can be subdivided into rule-based IP geolocation algorithms and learning-based IP geolocation algorithms according to the different methods; thanks to the real-time andrealistic nature of network measurement data, network-measurement-based methods have gradually become the main research direction in the field of IP geolocation research [6]. The rapid development of IPv6 networks has seen a rise in demand for location-based Internet applications; the previous IPv4 location algorithms did not achieve the same performance in IPv6. For researchers, the design of IP geolocation algorithms is based on IP protocols and the rules of network environments where IP protocols are deployed, and the design of IPv6 is proposed using IPv4 as a blueprint. There are differences between them, but at the same time, there are also similarities, so there is no split between IPv4 location algorithms and IPv6 location algorithms, just because of the differences in the IP protocols and network environment rules they face. Compared with IPv4, IPv6 has changed differently in terms of address pools, access methods, protocols, traffic characteristics, and AS-domain-level paths [7]. In a study related to the traditional IPv4 geolocation algorithm, IPv6-CBG [8] shows that the geolocation error of IPv6 networks is about 30% higher than that of IPv4 in the same experimental region due to the high latency in IPv6 networks; the RNBG [9] algorithm relies on the delay distribution rules in IPv4 and the difference in the delay distribution caused by the topological changes in IPv6. The Corr-SLG [10] algorithm enhances the SLG algorithm in weakly connected networks by evaluating the positive and negative correlations of hosts' relative delay distances, but it cannot improve the localization accuracy for weakly connected hosts and degraded localization performance. In addition, [PERSON] [11] proposed that the traditional IPv4-based network measurement method is affected by the huge address pool of IPv6, and the measurement period becomes longer. In IPv6, some of the traditional IP geolocation algorithms are applied with increased time cost, storage cost, and geolocation errors, so we try to design high-performance IP geolocation algorithms under IPv6. Deep learning plays an important role in the field of IP localization based on network measurements. For IPv6, we introduce deep learning based on a priori knowledge of IP geolocation.