用混合模型提升物联网入侵检测准确率，实测效果优于单一模型。
Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset
- 融合随机森林、XGBoost等模型，通过投票机制构建混合分类器。
- 在IoT-23数据集上，混合模型在二分类和多分类任务中均表现更优。
- 适合关注物联网安全与机器学习融合的开发者与研究人员。
物联网（IoT）的快速发展推动了产业变革，实现了前所未有的连接性与功能，但也带来了更多安全隐患，使物联网网络面临日益复杂的网络攻击。入侵检测系统（IDS）是缓解此类威胁的关键，而机器学习（ML）的最新进展为此提供了新的可能。本研究探索了一种混合方法，将随机森林（RF）、XGBoost、K近邻（KNN）和AdaBoost等独立模型结合，构建基于投票机制的混合分类器，以实现高效的物联网入侵检测。该集成方法利用各算法优势，提升准确率并应对数据复杂性和可扩展性挑战。基于广泛引用的IoT-23数据集——物联网网络安全研究中的主流基准，我们对混合分类器在二分类与多分类入侵检测任务中的性能进行了评估，并确保与现有文献的公平对比。结果表明，所提出的混合模型在物联网环境中表现出更强的鲁棒性与可扩展性，显著优于单一模型。本工作为构建能应对持续演化的网络威胁的智能入侵检测框架提供了重要支持。
原文摘要 · Abstract (English)
The rapid growth of the Internet of Things (IoT) has revolutionized industries, enabling unprecedented connectivity and functionality. However, this expansion also increases vulnerabilities, exposing IoT networks to increasingly sophisticated cyberattacks. Intrusion Detection Systems (IDS) are crucial for mitigating these threats, and recent advancements in Machine Learning (ML) offer promising avenues for improvement. This research explores a hybrid approach, combining several standalone ML models such as Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), and AdaBoost, in a voting-based hybrid classifier for effective IoT intrusion detection. This ensemble method leverages the strengths of individual algorithms to enhance accuracy and address challenges related to data complexity and scalability. Using the widely-cited IoT-23 dataset, a prominent benchmark in IoT cybersecurity research, we evaluate our hybrid classifiers for both binary and multi-class intrusion detection problems, ensuring a fair comparison with existing literature. Results demonstrate that our proposed hybrid models, designed for robustness and scalability, outperform standalone approaches in IoT environments. This work contributes to the development of advanced, intelligent IDS frameworks capable of addressing evolving cyber threats.
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