使用集群F1MI方法检测多重攻击的智能物联网应用程序
Vidhya Nagavel1, P T V Bhuvaneswari2, Parameswaran Ramesh2
1Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai, India. vin.vidhya612@gmail.com.
Scientific reports
|January 26, 2026
概括
智能安全物联网框架 (SSIF) 通过使用机器学习来检测网络威胁来增强物联网 (IoT) 安全性. 它有效地识别异常流量,为安全的物联网环境提供准确和高效的解决方案.
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:
- 物联网网络面临着来自不断的网络攻击的重大安全挑战.
- 机器学习 (ML) 提供可扩展的解决方案,用于识别大型物联网数据量中的异常活动.
研究的目的:
- 引入智能安全物联网框架 (SSIF) 以提高智能物联网网络的安全性.
- 使用Bot-IoT数据集用于框架开发和验证.
主要方法:
- 数据预处理以获得高质量的输入.
- 集群F1-MI特征工程用于选择信息属性.
- 基于集群的特征验证以消除特征相关性并提高效率.
- 使用ML分类器,包括SVM,随机森林,梯度提升,XGBoost和神经网络来进行威胁分类.
主要成果:
- 随机森林分类器在分类攻击类型时实现了超过0.97的准确性.
- 该框架有效地识别异常,触发电子邮件通知和报警.
- 该SSIF证明了有效性,可负担性和易于实施.
结论:
- 拟议的SSIF是一个强大而高效的解决方案,用于保护智能物联网环境免受Bot-IoT等网络威胁.
- 该框架的功能工程和ML分类方法显著提高了威胁检测能力.
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