Lohith Srikanth Pentapalli, Josette Riep, Justin Ouwerkerk, Kelly Cohen · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30576 · open-access PDF
Ein grundlegendes Paradoxon plagt die Implementierung von Machine Learning in der Cybersicherheit: Überwachte Modelle verallgemeinern sich schlecht zu neuen, Out-of-Distribution (OoD) Angriffen, während unüberwachte Modelle oft an schlechter Trennung leiden, was zu unmanagementbaren False-Alarm-Raten führt. Diese Herausforderung wird durch den BETH-Datenbestand, ein Kernel-Ebene-Benchmark, in dem die Schulungsdaten ausschließlich aus gutartigem und verdächtigem Insiderverhalten bestehen, während der Testsatz fast vollständig aus neuen Botnet-Aktivierungsangriffen besteht.
Machine translation (Meta M2M100); the English original follows.
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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