Dynamic ML Models for Evolving Networks: What, Where and How?

Paper in proceeding, 2026

Modern networks and threats evolve rapidly, challenging deployed machine-learning models. We address performance degradation of offline-trained models in post-failure traffic prediction and threat detection by proposing data-stream-based updating strategies for real-time optical network resilience evolution.