优化了对物联网网络的入侵检测,使用Cauchy-Gaussian混合进化特征选择
T Saranya1, S Indra Priyadharshini2
1School of Computer Science and Engineering,Vellore Institute of Technology, Chennai,TamilNadu, 600127, India.
Scientific reports
|December 20, 2025
概括
本研究引入了一种用于物联网 (IoT) 中的入侵检测系统 (IDS) 的新方法,使用主动特征选择和集体机器学习,实现高精度和低错误阳性率.
科学领域:
- 网络安全和网络工程 网络安全和网络工程
- 机器学习应用 机器学习应用
背景情况:
- 物联网 (IoT) 网络容易受到网络攻击,因为它们处理的敏感数据.
- 侵入检测系统 (IDS) 对物联网安全至关重要,但开发低复杂性的系统仍然是一个挑战.
- 现有的入侵分类方法经常与物联网环境的规模和复杂性作斗争.
研究的目的:
- 为物联网 (IoT) 提出一种新的,低复杂度的入侵检测系统 (IDS) 方法.
- 为了应对对资源有限的物联网设备构建高效和有效的入侵检测模型的挑战.
- 通过主动特征选择和整体机器学习来提高IDS的性能和降低其复杂性.
主要方法:
- 引入了一种新的Cauchy-Gaussian基因算术优化器驱动的基于差异的活性特征选择方法.
- 该方法采用两阶段的方法:基于特征变异的活跃样本学习的KD树表示,其次是优化器驱动的特征选择.
- 整体机器学习,特别是Bagging算法,与所选特征一起用于入侵分类.
主要成果:
- 拟议的方法使用包装算法实现了99.88% (CICIDS2017) 和99.72% (IoTID20) 的高准确率.
- 观察到一个显著低的假阳性率:0.000801 (CICIDS2017) 和0.000165 (IoTID20).
- 与传统的优化技术相比,主动特征选择方法明显降低了包装方法的复杂性.
结论:
- 新型主动特征选择方法与集体学习相结合,为物联网中的入侵检测系统提供了高效和低复杂性的解决方案.
- 考希-高斯基因算术优化器增强了特征选择多样性和融合,优于传统方法.
- 在基准数据集上验证的结果证实了该方法在物联网安全方面的精度和效率的优越性.
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