LATIDIA · Ciberseguridad

Optimización federada adaptativa y proximal para la detección de intrusiones de red transferibles

arXiv: 2610.19808v1Tipo de anuncio: nuevo Resumen: Los sistemas de detección de intrusiones de red (NIDS) basados en el aprendizaje profundo a menudo no detectan las clases de ataque que están ausentes de los datos de entrenamiento, lo que limita su robustez contra

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arXiv:2610.19808v1 Announce Type: new Abstract: Network Intrusion Detection Systems (NIDS) based on deep learning often fail to detect attack classes that are absent from training data, limiting their robustness against zero-day threats. This property called transferability across attack classes, remains underexplored especially in federated learning (FL) settings where data heterogeneity and client drift pose additional challenges. Prior work (TabFIDS) showed that FedAvg combined with bootstrapping and temporal averaging improves transferability for cross-device NIDS. In this paper, we explore whether other federated optimizers can do better. We compare two optimizers inspired by FedAdam and FedProx against the FedAvg baseline on the CIC-IDS 2017 dataset using an 11-client federated setup, sweep 10