自适应混合入侵检测框架 AHIDF：准确率0.89、AUC最高0.92
一项11月14日发表于《科学报告》的研究提出自适应混合入侵检测框架 AHIDF，把基于签名、基于机器学习与基于深度学习的三类检测器整合进单一框架，以应对日益复杂的网络攻击。
框架的核心是新提出的自适应置信度加权决策融合机制 ACWDF：它根据每个检测器近期的实际表现动态调整其输出权重。研究还提出一项综合评估指标 OIDRS，用于同时衡量检测能力、区分性能、误报行为、延迟与计算开销。
实验结果显示，该框架在各类网络攻击上取得0.89的准确率、0.88的精确率、0.86的召回率与0.87的 F1 分数；在受试者工作特征分析中，AUC 最高达到0.92。
作者来自 A.V.C. College of Engineering、Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology 与 Panimalar Engineering College 等印度院校。
作者表示，自适应决策融合提升了检测的稳健性与运行就绪度，该框架为当下的网络安全环境提供了一种可扩展的实现路径。本研究未获专项经费资助。
Adaptive Hybrid Intrusion Detection Framework AHIDF Reaches 0.89 Accuracy and AUC up to 0.92
A study published November 14 in Scientific Reports proposes the Adaptive Hybrid Intrusion Detection Framework, AHIDF, which combines signature-based, machine learning-based and deep learning-based detectors in a single framework, in response to increasingly sophisticated cyberattacks that strain single-detector, static rule-based systems.
At its core is a new Adaptive Confidence-Weighted Decision Fusion mechanism, ACWDF, which dynamically weights each detector's output based on the detector's effectiveness in recent operations. The study also proposes an Operational Intrusion Detection Readiness Score, OIDRS, to assess detection capability, discriminative performance, false alarm behavior, latency and computational overhead together.
Experimental results showed accuracy of 0.89, precision of 0.88, recall of 0.86 and an F1 score of 0.87 across the categories of cyberattacks, with ROC analysis reaching an AUC of up to 0.92.
The authors are affiliated with A.V.C. College of Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology and Panimalar Engineering College in India.
The authors say the adaptive decision fusion enhances detection robustness and operational readiness, and that the framework offers a realistic, scalable approach for today's cybersecurity environment. The research received no specific grant from any funding agency.
来源：Scientific Reports
发布：GSM·美国（发布链接）
图片来源：图：Indiana University 的网络运营中心，墙面大屏显示网络状态地图。来源：Alan Levine，Wikimedia Commons，CC BY 2.0。 / Photo: The network operations center at Indiana University, with network status maps on the wall displays. Source: Alan Levine, Wikimedia Commons, CC BY 2.0.
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