HAMC-ID:用于入侵检测的混合基于注意力的元分类器
S Antony Joseph Raj1, M Madiajagan2
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Scientific reports
|December 6, 2025
概括
本研究介绍了HAMC-ID,这是一种用于增强入侵检测系统 (IDS) 的新型双层堆叠组合框架. HAMC-ID显著提高了对复杂网络威胁的检测准确度和稳定性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络入侵检测 网络入侵检测
背景情况:
- 由于网络威胁的增加,传统的入侵检测系统 (IDS) 难以实现灵活性和准确性.
- 现代网络的复杂性需要先进的检测机制.
研究的目的:
- 提出一个新的双层堆叠组合框架,HAMC-ID,用于增强入侵检测.
- 提高入侵检测系统的准确性,精度,回忆和F1分数.
主要方法:
- 开发了一个双层堆叠组合框架 (HAMC-ID).
- 在0级使用异质基准分类器 (极端梯度提升,额外树,物流回归).
- 采用双向长期短期记忆网络,注意作为1级的元分类器,整合了像逻辑,信心和这样的元特征.
主要成果:
- 在个人分类器和传统集体方法相比,HAMC-ID表现优越.
- 在UNSW-NB15和CICIDS2017数据集的准确性,精度,回忆和F1分数上观察到持续的改善.
- 该框架证明了对二进制和多类分类任务的有效性.
结论:
- HAMC-ID为实际的网络安全应用提供了强大而多功能解决方案.
- 拟议的框架有效地解决了传统IDS在各种网络场景中的局限性.
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