Lohith Srikanth Pentapalli, Josette Riep, Justin Ouwerkerk, Kelly Cohen · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30576 · open-access PDF
Sebuah paradoks fundamental mengganggu penyebaran Machine Learning di cybersecurity: model yang diawasi menggeneralisasi buruk untuk serangan baru, Out-of-Distribution (OoD), sedangkan model yang tidak diawasi sering menderita pemisahan yang buruk, menyebabkan tingkat false-alarm yang tidak dapat dikendalikan. tantangan ini diilustrasikan oleh dataset BETH, benchmark tingkat inti di mana data pelatihan hanya terdiri dari perilaku internal yang baik dan mencurigakan, sedangkan set tes hampir sepenuhnya terdiri dari serangan aktivasi botnet baru.
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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