NetCast: Learning Compact Latent Representations from Network Traces

Network foundation models promise reusable representations for diverse traffic analysis tasks, but existing models can produce concentrated embedding spaces and show weak alignment with interpretable traffic statistics, raising questions about whether their representations capture meaningful traffic behavior. We translate these limitations into two concrete design objectives: learning behavioral structure through future burst-state prediction and encouraging representation diversity through distributional regularization. Guided by these objectives, we introduce NetCast, which predicts future burst states from causal flow history using a shared encoder that combines packet content with traffic metadata and a state-distribution regularizer that encourages variation across encoded bursts. We evaluate NetCast across four traffic datasets through centered kernel alignment (CKA) with measured network statistics and intrinsic analyses of embedding geometry. NetCast achieves the highest reported mean CKA of 0.104 and the lowest average pairwise cosine similarity of 0.55 among the compared trained models, while reducing angular concentration on every dataset. A regularizer ablation further supports the contribution of distributional regularization to this geometric improvement. Together, these results demonstrate the potential of burst-state prediction with distributional regularization to produce better-structured network representations that more closely reflect measurable traffic behavior.