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改进了使用强大的统计特征进行时间物联网设备识别

Nik Aqil¹, Faiz Zaki¹, Firdaus Afifi^1,2

¹Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

PeerJ. Computer science

|August 15, 2024

概括

本研究引入了一套使用有效载荷长度识别物联网 (IoT) 设备的新功能. 这种方法通过随着时间的推移保持高精度来增强网络安全,减少频繁重新培训的需要.

科学领域:

计算机科学计算机科学
网络安全网络安全
机器学习机器学习

背景情况:

物联网 (IoT) 设备的扩散需要强大的网络识别和安全方法.
目前基于机器学习的物联网识别解决方案因数据漂移而面临性能下降,需要昂贵的再培训.
准确的物联网设备识别对于网络可见性和提高整体网络安全至关重要.

研究的目的:

开发一种稳定有效的功能集,用于识别物联网 (IoT) 设备.
提高物联网设备识别模型的性能,减少物联网设备识别模型的重新训练频率.
优化学习过程,以便更轻松地集成新的物联网设备.

主要方法:

开发了一套新的功能集,利用有效载荷长度来捕获独特的物联网设备特性.
拟议的功能集与随机森林和一对一休息分类器集成,以优化学习.
采用了每周数据集的细分,以确保在不同时期进行严格和时间意识的评估.

主要成果:

拟议的功能集在物联网交通轨迹数据集的所有评估周中保持了超过80%的准确性.
该方法在自主收集的物联网-FSCIT数据集上显示,随着时间的推移,准确度提高了10.13%.
新功能集在物联网设备识别方面表现优于精选的基准研究.

结论:

基于有效载荷长度的功能集为物联网 (IoT) 设备识别提供了稳定而准确的解决方案.
这种方法通过提供可靠的设备识别来增强网络安全,并减少重新培训的计算开销.
该方法有助于有效地将新的物联网设备添加到网络中,提高了适应性和安全性.

关键词:

降低精度降低了精度.设备识别设备识别物联网的物联网,就是物联网.物联网安全物联网安全物联网安全机器学习机器学习网络流量分类网络流量分类.交通分析交通分析

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