Real-Time Anomaly Detection in BGP: Challenges, IPv6 Considerations, and Machine Learning Opportunities

Border Gateway Protocol (BGP) anomalies, such as route hijacks, misconfigurations, and worm-induced disruptions, significantly threaten global Internet stability. While machine learning (ML) methods have improved anomaly detection, critical challenges persist: limited availability of comprehensive IPv6/IPv4 labeled datasets and significant preprocessing delays that prevent real-time anomaly classification. This research deals with extending established dataset-generation methods to create robust, parallel datasets for IPv4 and IPv6 anomalies. It further evaluates advanced ML models, including LSTM, Transformers, and Graph Neural Networks (GNNs), specifically focusing on reducing detection latency. Our approach aims to integrate optimized preprocessing workflows, diversified datasets, and streaming-based inference. We expect this will improve anomaly detection accuracy and speed, moving closer to practical real-time BGP anomaly detection.