# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 2 Related Work
### Analysis of Existing Advanced IP Geolocation Algorithms  
The existing IP geolocation idea is shown in Figure 1. IPv6-CBG [8] is an early IPv6-based geolocation study, and the algorithm improves the accuracy of IPv6 geolocation algorithms by constructing a delay distance mapping function to evaluate IPv6 geolocation tasks. Although the algorithm is an IPv6 geolocation study based on the delay value, the delay value itself is affected by the network congestion, which makes it impossible to represent the real physical distance. In addition, the high delays in IPv6 environments increase the actual geolocation errors of the algorithm. Corr-SLG [10] is an advanced rule-based IP geolocation algorithm that evaluates the positive and negative strong correlations of landmarks relative to the delay distance and classifies landmarks into positive strong correlation, negative strong correlation, and weak correlation, enhancing the application of SLG in weakly connected networks at the expanse of hosts in the weak correlation class unable to be accurately located. The performance of the rule-based approach relies on host partitioning, which follows a preassumed linear rule. In weakly connected IPv6,the presence of a large number of hosts in the weakly correlated class increases the true geolocation error of the algorithm.  
The TNN algorithm [23] and the MLP-Geo algorithm [24] give up observing the relationship between delay values and physical distances. The TNN algorithm evaluates the geolocation information of the target IP by training the neural network to learn the mapping function of delay similarity markers, which has excellent geolocation performance. However, dynamic updating routes can affect the evaluation of delayed similarity. There exist delay similar hosts with a real physical distance that is far, ignoring the need for regional delay similarity for fine-grained reasonable region constraints. The MLP-Geo algorithm adds a region constraint to the region delay similarity assumption by adding stable paths, which makes up for the lack of the region constraint in the TNN algorithm and enhances the application of region delay similarity in IP geolocation algorithms. However, it is worth noting that the stable path is a set of paths composed of stable routing device connections, which can effectively represent the coarse granularity of packet traces in the network. However, the stable path tends to focus on the side of the measurement path near the probe, and there are fewer stable routes on the side near the target IP. The stable path ignores the features of the measurement path near the target host and, likewise, cannot constrain the path after the stable path direction. In other words, the stable path cannot constrain the fine-grained region and path direction of the target IP.  
Delay similarity [23] helps us in the task of geolocating IPv6 hosts. Since different regions in the real world may also have similar host delay, delay similarity cannot be a decisive factor, and it requires a fine-grained geographic constraint. The difference between our work and existing methods is that we focus on the regions that cannot be covered by stable paths, extract geographically constrained routing features hierarchically from landmark measurement paths, approximate the target IP to a fine-grained trusted region, and focus on host delay similarity within this region for accurate and robust IPv6 geolocation.