LATIDIA · Ciberseguridad

Ataques de evasión en el entrenamiento adversario basado en costo-utilidad para AutoML en línea en redes IoT

arXiv: 2609.31981v1Tipo de anuncio: nuevo Resumen: A medida que las redes de Internet de las cosas (IoT) dependen cada vez más del aprendizaje automático para anomalías, malware, detección de intrusiones y monitoreo de redes, dichos sistemas se han convertido en

La noticia

arXiv:2609.31981v1 Announce Type: new Abstract: As Internet of Things (IoT) networks increasingly depend on machine learning for anomaly, malware, intrusion detection, and network monitoring, such systems have become attractive targets for evasion attacks. Evasion attacks pose a major security risk because an adversary intentionally modifies input data to mislead a trained model into producing incorrect predictions while evading detection. This study evaluates the impact of black-box evasion attacks on a cost-utility-based adversarial training defense strategy in an Online AutoML context for IoT networks. Specifically, evasion attacks were applied to online learners, including Hoeffding Tree (HT), Leveraging Bagging (LB), Streaming Random Patches (SRP), Hoeffding Adaptive Tree (HAT), and Adaptive Random Forest