Newest papers

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

Chinese

一个根本的悖论困扰了机器学习在网络安全领域的部署:受监督的模型对新型、非分布式(OoD)攻击的普遍化不佳,而不受监督的模型往往会遭受不良的分离性,导致无法管理的假警报率。这个挑战是由BETH数据集,一个内核级别的基准,其中培训数据仅由良性和可疑的内部行为组成,而测试集几乎完全由新型botnet激活攻击组成。

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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