强大的物联网安全使用隔离森林和一类SVM算法
Amna Zahoor1, Waseem Abbasi2, Muhammad Zeeshan Babar3
1Department of Computer Science, The University of Lahore, Sargodha Campus, Sargodha, 40100, Pakistan.
Scientific reports
|October 21, 2025
概括
本研究介绍了物联网 (IoT) 网络的强大的异常检测框架. 一级支持矢量机 (OCSVM) 在识别资源有限的物联网设备上的网络攻击方面表现出卓越的性能.
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:
- 物联网设备因资源限制和安全协议限制而面临越来越多的网络攻击.
- 有效的异常检测对于保护相互连接的物联网环境至关重要.
研究的目的:
- 为物联网网络开发和评估一个强大的异常检测框架.
- 为了比较无监督机器学习模型的性能,特别是隔离森林 (IF) 和一类支持矢量机器 (OCSVM).
主要方法:
- 使用TON_IoT数据集来评估IF,OCSVM,以及一个综合评分方法 (CSAD).
- 使用特征重要性分析,交叉验证和超参数调整以提高模型可靠性.
- 评估了对抗标签翻转中毒攻击的模型弹性,并使用LIME进行解释性.
主要成果:
- 在精度,回忆和准确性方面,OCSVM的表现优于IF和CSAD.
- 该框架展示了对物联网环境的有效异常检测能力.
- 轻量级的无监督算法被证明适用于低资源的物联网异常检测.
结论:
- 无监督机器学习模型,特别是OCSVM,为物联网异常检测提供了有效的解决方案.
- 拟议的框架为增强物联网网络安全提供了一种可靠和可解释的方法.
- 轻量级算法对于资源有限的物联网安全应用程序是可行的.
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