# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 2 Related Work
### IP Geolocation Algorithm  
IP geolocation techniques fall into two main categories: database-based queries [12] and network-based measurements [13]. IP geolocation based on database query is realized by querying the corresponding location information of the target IP recorded in the IP geolocation database [14]. The IP geolocation database records and maintains a series of geographic information records of IP addresses [4]. The IP geolocation database query method is faster, but its data reliability cannot be verified [15]. On the other hand, data integrity and real-time performance are poor because data resources are updated slowly [16]. Contemporary researchers more often use multidatabase federated queries and learning-based methods to improve their geolocation performance [17] and databases [18]. Although having different degrees of accuracy improvement, they still cannot be used to accomplish fine-grained IP geolocation tasks. The network-measurement-based IP geolocation method obtains the data [19] of Internet host network communication using active or passive network measurement methods. Figure 1: IP geolocation algorithm analysis. The measurement data are analyzed and processed to establish the mapping relationship with the geographic location to achieve IP geolocation. According to the differences in research methods that are divided into two categories: (1) There are the rule-based IP geolocation algorithms such as SLG [20], Corr-SLG [10], RNBG [9], and Geo-PoP [21]; network characteristics combined with the analysis of the real geographic location are used to discover the corresponding rules. The core rule of SLG is to filter the landmarks with the smallest relative delay to the target IP, based on the assumption that the relative delay size is proportional to the distance. However, this rule has poor performance in weak connections. RNBG is based on the assumption that the delay size is proportional to the real physical distance. The core rule is to screen the largest one-hop delay on the path and use the neighboring router with the largest delay as the city-level boundary route, which circumvents the impact of network connectivity on IP geolocation accuracy, but cannot complete the fine-grained IP geolocation task. Rule-based IP geolocation algorithms have good performance in the networks to which their rules apply, but the rules cannot be applied to all networks, Therefore, the generalization ability of this type of algorithm is poor. (2) The learning-based IP geolocation algorithms include LBG [22], TNN [23], MLP-Geo [24], GeoCET [25], etc. LBG creatively introduces regional demographic factor features and uses machine learning methods to statistically analyze network measurement data to complete city-level IP geolocation tasks. GeoCET completes IP geolocation tasks by combining elliptical trajectory constraints with machine learning techniques. Learning-based IP geolocation algorithms can better improve the generalization ability of IP geolocation algorithms. However, we need to pay attention to the interpretability of IP geolocation models in deep learning. As more local servers are deployed in cloud-based solutions, the number of landmarks available for collection becomes smaller, posing new challenges for measurement-based IP geolocation [10]. The combination of IPv6 and the Internet of Things (IoT) [26] has given birth to many new IPv6 application scenarios [27]. There are also studies on IP geolocation algorithms based on the application scenarios of IoT devices. For example, GeoCAM [28] select cameras as landmarks in IoT application scenarios to design fine-grained IP geolocation algorithms. [PERSON] [10] proposed a new landmark collection technique that uses mobile devices to collect WiFi [29] landmarks in public places such as hotels, banks, and supermarkets. [PERSON] [30] proposed an IPv6 geolocation algorithm based on home routing devices, which first collects MAC addresses that are embedded in IPv6 addresses and, then, queries the target IP geolocation by WiFi BSSIDs [31].