用混合模型提升物联网入侵检测准确率与可扩展性
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models
- 采用集成投票机制构建混合分类器，融合多种机器学习算法
- 在IoT23数据集上，混合模型在二分类和多分类任务中均达98%以上准确率
- 适合关注物联网安全、需高精度检测的系统设计者参考
物联网系统安全性依赖于入侵检测技术，因网络对网络攻击日益敏感。基于IoT23数据集，本研究探索了多种机器学习（ML）与深度学习（DL）模型，以及混合模型在二分类和多分类入侵检测中的应用。独立模型包括随机森林（RF）、极端梯度提升（XGBoost）、人工神经网络（ANN）、K近邻（KNN）、支持向量机（SVM）和卷积神经网络（CNN）。此外，构建了两种基于投票机制的混合模型：一种用于二分类，另一种用于多分类，整合了RF、XGBoost、AdaBoost、KNN和SVM。所有模型均通过精确率、召回率、准确率和F1分数进行评估，并对比性能。结果表明，混合模型在准确率与可扩展性方面显著优于单一模型，为物联网入侵检测系统提供了有效解决方案。
原文摘要 · Abstract (English)
Maintaining security in IoT systems depends on intrusion detection since these networks' sensitivity to cyber-attacks is growing. Based on the IoT23 dataset, this study explores the use of several Machine Learning (ML) and Deep Learning (DL) along with the hybrid models for binary and multi-class intrusion detection. The standalone machine and deep learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) were used. Furthermore, two hybrid models were created by combining machine learning techniques: RF, XGBoost, AdaBoost, KNN, and SVM and these hybrid models were voting based hybrid classifier. Where one is for binary, and the other one is for multi-class classification. These models vi were tested using precision, recall, accuracy, and F1-score criteria and compared the performance of each model. This work thoroughly explains how hybrid, standalone ML and DL techniques could improve IDS (Intrusion Detection System) in terms of accuracy and scalability in IoT (Internet of Things).
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