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机器学习用于物联网 (IoT) 设备识别:一项比较研究
Hamid Tahaei1, Anqi Liu2, Hamid Forooghikian3
1Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了用于物联网 (IoT) 设备识别的二进制绿色狼优化器 (BGWO). BGWO显著减少了功能,提高了分类准确性,提供了一个强大的网络安全解决方案.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 数以百万计的连接物联网 (IoT) 设备带来了重大的安全挑战.
- 物联网设备识别对于网络安全至关重要,通常使用机器学习 (ML) 在设备指纹的网络流.
- 现有的研究缺乏对ML分类器和特征选择 (FS) 算法的比较分析,用于物联网设备识别.
研究的目的:
- 综合评估ML分类器和FS方法用于物联网设备识别的性能.
- 在各种ML分类器中评估基于过器和包装的FS方法的有效性.
- 介绍和评估二元绿狼优化器 (BGWO) 用于物联网设备指纹.
主要方法:
- 多个ML分类器的比较性能评估.
- 基于过器和包装器的特征选择 (FS) 方法的评估.
- 使用两个数据集实现和比较二元绿狼优化器 (BGWO) 与传统的ML分类器.
主要成果:
- BGWO实现了显著的特征集减少:数据集1的85.11%,数据集2的73.33%.
- BGWO的分类准确度很高:数据集1的98.51%和数据集2的99.8%.
- 基于包装的FS方法在减少特征集方面表现出有效性.
结论:
- 二元绿狼优化器 (BGWO) 在减少特征维度和提高物联网设备识别分类准确度方面表现出强大的能力.
- BGWO介绍了一种有前途的元启发式算法,用于增强物联网网络安全.
- 包装方法是有效的功能减少物联网设备指纹.
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