适应型树:树木组合,以增强不平衡的入侵分类
Mahbub E Sobhani1, Anika Tasnim Rodela1, Dewan Md Farid2
1Department of Computer Science and Engineering, United International University, United City, Dhaka, Bangladesh.
PloS one
|September 19, 2025
概括
适应TreeHive通过选择信息化数据实例来有效处理不平衡的入侵检测. 这种新的方法优于传统的采样技术,改善了网络安全防御网络攻击.
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:
- 不平衡的入侵分类由于少数阶级实例带来了挑战.
- 传统的方法,如过量采样 (SMOTE) 和不足采样,具有诸如过拟合和数据丢失等局限性.
- 使用集体学习方法 (随机森林,包装,提升),但可以改进不平衡数据.
研究的目的:
- 提出一种新的方法,即适应树,用于选择信息实例以解决不平衡的入侵数据.
- 为了评估适应TreeHive与传统数据平衡和组合方法的性能.
- 为了减少入侵检测系统中的计算能力和执行时间要求.
主要方法:
- 开发了Adaptive TreeHive,用于选择信息化的多数和少数实例.
- 应用过量采样和不足采样技术与信息化实例选择结合.
- 使用的整体算法:随机森林 (C4.5基础),包装 (天真贝叶斯基础) 和提升 (AdaBoost,C4.5基础).
主要成果:
- 适应型TreeHive在五个大规模基准数据集 (NSL-KDD,UNSW-NB15,CIC-IDS2017,CSE-CIC-IDS2018,CICDDoS2019) 中表现出卓越的性能.
- 实现了高准确率,包括NSL-KDD上的99.96%和CIC-IDS2017.2017上的99.83%.
- 超越了标准的不足抽样和过量抽样数据平衡方法.
结论:
- 适应型TreeHive有效地解决了入侵检测中的不平衡比率和高维度.
- 拟议的方法为不平衡的网络入侵数据提供了与传统集体分类器相比的显著改进.
- 适应型TreeHive通过提供更准确,更有效的入侵分类来增强网络安全.
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