Search

IPv6 geolocation is necessary for many location-based Internet services. However, the accuracy of the current IPv6 geolocation methods including machine-learning-based or deep-learning-based location algorithms are unsatisfactory for users. Strong geographic correlation is observed for measurement path features close to the target IP, so previous methods focused more on stable paths in the vicinity of the probe. Based on this, this paper proposes a new IPv6 geolocation algorithm, SubvectorS_Geo, which is mainly divided into three steps: firstly, it filters geographically relevant routing feature codes layer by layer to approximate the fine-grained trusted region of the target; secondly, it extracts delay vectors into the trusted region; thirdly, it evaluates the vector similarity to determine the final target geolocation information. The final experiments show that the median error distance range is 7.025 km to 9.709 km on three real datasets (Shanghai, New York State, and Tokyo). Compared with the advanced method, the median distance error distance is reduced by at least 6.8% and the average error distance is reduced by at least 9.2%. [ABSTRACT FROM AUTHOR]

IP geolocation is a technique for inferring geolocation of a device based on characteristics of its IP addresses such as network measurements or queryable information. Traditional IPv4 geolocation methods are often inapplicable to IPv6 networks or produce unsatisfactory results due to the imperfection of network routing and the sparsity of IPv6 addresses. To improve the accuracy of IPv6 geolocation, we proposed GWS-Geo, a street-level IPv6 geolocation model based on Graph Neural Network(GNN). Our model includes preprocessing, pre-training, an improved GraphSAGE algorithm, and hierarchical classification. In the preprocessing step, we process node features and transform them into node embeddings, and transform landmarks' latitude and longitude coordinates into area numbers. During pretraining, we assign weights to edges between IP addresses and input node information into the improved GraphSAGE algorithm. After pruning according to edge weights, the improved GraphSAGE executes graph convolution operations. Finally, we use hierarchical classification to divide the geolocation into finer granularity to obtain the location of the target IP address. Experimental results on three datasets (covering the Tokyo, New York, and Shanghai areas) show that the median error distance is within a range of 5.53km to 9.46 km, which is close to the level of street-level accuracy. Compared to popular geolocation algorithms such as SLG, IRLD, and MLP-Geo, our model reduces the median error distance by at least 15.99% and the average error distance by at least 16.36%. [ABSTRACT FROM AUTHOR]