Large-Scale IP Usage Identification via Deep Ensemble Learning (Student Abstract)
Keywords:IP Scenario, IP Address Profiling, Deep Ensemble Learning, Tabular Data Learning
AbstractUnderstanding users' behavior via IP addresses is essential towards numerous practical IP-based applications such as online content delivery, fraud prevention, and many others. Among which profiling IP address has been extensively studied, such as IP geolocation and anomaly detection. However, less is known about the scenario of an IP address, e.g., dedicated enterprise network or home broadband. In this work, we initiate the first attempt to address a large-scale IP scenario prediction problem. Specifically, we collect IP scenario data from four regions and propose a novel deep ensemble learning-based model to learn IP assignment rules and complex feature interactions. Extensive experiments support that our method can make accurate IP scenario identification and generalize from data in one region to another.
How to Cite
Wang, Z., Zhou, F., Zhang, K., & Wang, Y. (2022). Large-Scale IP Usage Identification via Deep Ensemble Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13077-13078. https://doi.org/10.1609/aaai.v36i11.21675
AAAI Student Abstract and Poster Program