Lohith Srikanth Pentapalli, Josette Riep, Justin Ouwerkerk, Kelly Cohen · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30576 · open-access PDF
サイバーセキュリティにおける機械学習の展開には根本的な矛盾がある:監督されたモデルは、新規のOoD(Out-of-Distribution)攻撃に悪く一般化する一方で、監督されていないモデルはしばしば不十分な分離性に苦しみ、管理できない偽警報率につながる。この課題は、BETHデータセット、コアレベンチャーレベルのベンチマークで例えられ、トレーニングデータは単に良性および疑わしい内部の行動で構成され、テストセットはほぼ完全に新規のボットネットアクティベーション攻撃で構成されています。
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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