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一个基于机器学习的框架,用于使用Web流量识别物联网设备.

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  • 1Department of Information Security, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.

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概括
此摘要是机器生成的。

本研究介绍了用于识别物联网 (IoT) 设备的精度提升模型 (ABM),以提高智能环境中的网络安全性. 机器学习方法在检测各种物联网设备方面实现了高精度,减轻了隐私风险.

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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:

  • 在智能家居和办公室中物联网 (IoT) 设备的扩散引入了重要的隐私和数据安全风险.
  • 识别未知的物联网设备对于网络管理至关重要,以防止漏洞和数据盗窃.
  • 随着连接设备的数量不断增加,手动设备识别变得不切实际,需要自动化解决方案.

研究的目的:

  • 开发和评估一种自动机器学习模型,用于准确识别各种物联网设备.
  • 通过有效地区分合法设备和潜在的入侵者来增强网络安全.
  • 为了应对大量连接物联网设备的管理和保护网络的挑战.

主要方法:

  • 提出了一种采用整体机器学习技术的精度提升模型 (ABM).
  • 使用随机森林 (RF) 和极端梯度提升 (XGB) 作为具有适应性提升的基础学习者.
  • 实现了功能工程和交叉验证,以实现强大的物联网设备识别.

主要成果:

  • 拟议的整体模型在识别物联网设备方面实现了91%的高精度.
  • 在精度,回忆和F1得分方面表现出强的表现,所有得分为93%.
  • 使用来自公共存储库的物联网设备识别数据集进行实验验证.

结论:

  • 精度提升模型有效地识别各种物联网设备,包括灯,恒温器和安全摄像头.
  • 开发的机器学习方法为保护智能环境提供了可扩展和自动化的解决方案.
  • 该模型的高性能指标证实了其在网络安全中的实际应用潜力.