English(EN) Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection

新的RECAL框架通过平衡关系分析来改进入侵检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一个名为RECAL的新框架，用于基于来源的入侵检测系统（PIDS），该框架解决了平等对待所有关系的问题。这种新方法使用面向关系的平衡掩码图学习来更好地识别稀有交互模式，这对于检测高级持续性威胁（APT）至关重要。通过将重建误差与每种关系的良性分布进行校准，RECAL旨在减少误报和漏报。在DARPA E3数据集上的测试中，RECAL取得了近乎完美的F1分数，并显著降低了与现有基线相比的误报率。 AI

影响通过改进对复杂系统交互的分析，增强了网络安全中的异常检测能力。

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arXiv cs.LG TIER_1 English(EN) · Lijie Zheng, Ji He, Alessandro Brighente, Yulong Shen, Mauro Conti · 2026-09-16 04:00

并非所有关系都平等：基于来源的入侵检测的关系平衡和校准图学习

arXiv:2609.16462v1 Announce Type: cross Abstract: Provenance-Based Intrusion Detection Systems (PIDSs) detect Advanced Persistent Threats (APTs) by analyzing system interactions. However, existing methods largely treat relations uniformly, overlooking statistical heterogeneity; i…

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并非所有关系都平等：基于来源的入侵检测的关系平衡和校准图学习

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