Yifan Wang, Dongliang Xu, Xiaoyong Li, Jing Li, Peter S. Pan, Yue Yao · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31000 · open-access PDF
大型语言模型(LLMs)已经展示了网络安全的强大潜力,导致对基于LLM的自动网络安全日志分析日志分析的兴趣日益增加。然而,目前的数据集(1)规模有限;(2)主要依赖单源证据;以及(3)仅具有二进制或多类攻击标签,没有对推理过程的密集注释。 为了解决这些局限性,SecLog-Bench具有三个关键功能。 首先,我们构建了一个可扩展的模拟平台,涵盖29个攻击场景,支持重复执行和新场景的整合,并允许可扩展的基因
Machine translation (Meta M2M100); the English original follows.
Large language models (LLMs) have shown strong potential for cybersecurity, leading to growing interest in LLM-based automated cybersecurity log analysis. However, current datasets (1) are limited in scale; (2) rely mainly on single-source evidence; and (3) have only binary or multi-class attack labels, without dense annotations of the reasoning process. To address these limitations, SecLog-Bench has three key features. First, we build an extensible simulation platform that covers 29 attack scenarios, supports repeated execution and the integration of new scenarios, and enables scalable genera
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