Measuring and classifying IP usage scenarios: a continuous neural trees approach.

Understanding user behavior via IP addresses is a crucial measure towards numerous pragmatic IP-based applications, including online content delivery, fraud prevention, marketing intelligence, and others. While profiling IP addresses through methods like IP geolocation and anomaly detection has been thoroughly studied, the function of an IP address-e.g., whether it pertains to a private enterprise network or a home broadband-remains underexplored. In this work, we initiate the first attempt to address the IP usage scenario classification problem. We collect data consisting of IP addresses from four large-scale regions. A novel continuous neural tree-based ensemble model is proposed to learn IP assignment rules and complex feature interactions. We conduct extensive experiments to evaluate our model in terms of classification accuracy and generalizability. Our results demonstrate that the proposed model is capable of efficiently uncovering significant higher-order feature interactions that enhance IP usage scenario classification, while also possessing the ability to generalize from the source region to the target one.

Learning Spatiotemporal Manifold Representation for Probabilistic Land Deformation Prediction.

Landslides refer to occurrences of massive ground movements due to geological (and meteorological) factors, and can have disastrous impacts on property, economy, and even lead to the loss of life. The advances in remote sensing provide accurate and continuous terrain monitoring, enabling the study and analysis of land deformation which, in turn, can be used for land deformation prediction. Prior studies either rely on predefined factors and patterns or model static land observations without considering the subtle interactions between different point locations and the dynamic changes of the surface conditions, causing the prediction model to be less generalized and unable to capture the temporal deformation characteristics. To address these issues, we present DyLand, a dynamic manifold learning framework that models the dynamic structures of the terrain surface. We contribute to the land deformation prediction literature in four directions. First, DyLand learns the spatial connections of interferometric synthetic aperture radar (InSAR) measurements and estimates the conditional distributions on a dynamic terrain manifold with a novel normalizing flow-based method. Second, instead of modeling the stable terrains, we incorporate surface permutations and capture the innate dynamics of the land surface while allowing for tractable likelihood estimations on the manifold. Third, we formulate the spatiotemporal learning of land deformations as a dynamic system and unify the learning of spatial embeddings and surface deformation. Finally, extensive experiments on curated real-world InSAR datasets (land slopes prone to landslides) show that DyLand outperforms existing benchmark models.