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Bridging the Closed-World Gap: Interpretable Anomaly Detection via a Hybrid TSK-FCM Ensemble on the BETH Dataset

Lohith Srikanth Pentapalli, Josette Riep, Justin Ouwerkerk, Kelly Cohen · 2026 · arXiv

DOI: 10.48550/arXiv.2610.30576 · open-access PDF

Spanish

Una paradoja fundamental plaga el despliegue del Machine Learning en la ciberseguridad: los modelos supervisados generalizan mal a nuevos ataques fuera de distribución (OoD), mientras que los modelos no supervisados a menudo sufren de mala separabilidad, lo que conduce a tasas de falsas alarmas no gestionables. Este desafío es ejemplificado por el conjunto de datos BETH, un benchmark a nivel de núcleo en el que los datos de entrenamiento consisten únicamente en comportamientos de insider benignos y sospechosos, mientras que el conjunto de pruebas consiste casi enteramente en ataques de activación de botnet nuevos.

Machine translation (Meta M2M100); the English original follows.

Abstract

A fundamental paradox plagues the deployment of Machine Learning in cybersecurity: supervised models generalize poorly to novel, Out-of-Distribution (OoD) attacks, whereas unsupervised models often suffer from poor separability, leading to unmanageable false-alarm rates. This challenge is exemplified by the BETH dataset, a kernel-level benchmark in which the training data consists solely of benign and suspicious insider behaviors, whereas the test set consists almost entirely of novel botnet activation attacks. To bridge this detection gap, we propose a Hybrid Fuzzy Ensemble that synergizes th

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