优化物联网入侵检测,使用基于等号相似性的数据集平衡和混合深度学习
Arvind Prasad1, Wael Mohammad Alenazy2, Naved Ahmad3
1Department of Computer Engineering & Applications, GLA University, Mathura, India. arvind.prasad@gla.ac.in.
Scientific reports
|August 22, 2025
概括
物联网网络的网络安全威胁正在上升. 一种新的方法,即基于共弦相似度的多数类减少 (CSMCR),有效地平衡入侵检测数据集,改善威胁检测并减少训练时间.
科学领域:
- 网络安全
- 机器学习
- 物联网 (物联网)
背景情况:
- 物联网 (IoT) 设备的普及显著扩大了网络威胁的攻击面.
- 侵入检测系统 (IDS) 对于实时威胁缓解至关重要,但在攻击实例罕见的情况下,它们会与不平衡的数据集作斗争.
- 在不平衡的数据上训练IDS导致有偏见的模型,无法检测入侵.
研究的目的:
- 引入一种新的数据平衡技术,即基于共弦相似度的多数类减少 (CSMCR),用于改进入侵检测系统 (IDS).
- 开发和评估混合深度学习模型,以提高物联网网络安全的功能提取和分类.
- 证明CSMCR在防止过度安装和改善模型解释性的有效性.
主要方法:
- 通过分析特征相似性,CSMCR可以选择性地删除多余的多数类样本,从而保持数据完整性和多样性.
- 一个混合深度学习模型整合了RegNet和FBNet架构,用于高级功能提取.
- 数据集以1:1的比例进行平衡,以优化模型性能并防止偏差.
主要成果:
- 与SMOTE等传统方法相比,CSMCR技术取得了更高的性能.
- 拟议的混合型号获得了高F1分:RT-IoT2022的0.9758分和UNSW Bot-IoT的0.9275分.
- CSMCR减少了53%的培训时间,并且在多数与少数的比例上表现出最佳表现.
结论:
- 在物联网环境中平衡IDS数据集的强大,可扩展和计算效率高的策略.
- 最佳的数据平衡显著提高了IDS的性能,可解释性,并减少了计算开销.
- 这些发现强调了解决数据不平衡对于有效的物联网网络安全的重要性.
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