对比五种机器学习模型在真实物联网环境下的入侵检测效果，随机森林表现最优。
Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks
- 用五个机器学习算法对比检测物联网攻击，基于真实设备生成的数据集。
- 随机森林模型在攻击分类上达到0.99的F1分数，性能最佳。
- 适合关注物联网安全与智能检测的研究者和工程师参考。
物联网（IoT）正迅速发展并渗透至医疗、交通、智能家居等多个领域。尽管其带来诸多便利，但设备资源受限也带来了安全与隐私挑战。本文基于戈顿测试平台生成的Gotham2025数据集，该数据集包含78个模拟物联网设备，使用MQTT、CoAP和RTSP等协议，旨在提升物联网网络的抗攻击能力。研究对五种机器学习算法——随机森林、XGBoost、逻辑回归、朴素贝叶斯和深度神经网络进行了对比分析。结果表明，随机森林分类器在攻击检测任务中表现最佳，实现了0.99的F1分数。
原文摘要 · Abstract (English)
The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more. Despite the benefits of using IoT devices, they present several challenges. Given the significant role these devices play in our lives, it is crucial to address issues related to their security and privacy. These devices are limited in resources, which complicates their security and the protection of the data that they manage. The paper aims to examine intrusion detection systems using the Gotham2025 dataset, generated through the Gotham testbed, which consists of 78 emulated IoT devices utilising various protocols, including MQTT, CoAP, and RTSP, to assist in safeguarding IoT networks from attacks. We conduct a comparative analysis between five machine learning algorithms, including Random Forest, XGBoost, Logistic Regression, Naive Bayes, and Deep Neural Network. We demonstrate that the Random Forest Classifier was the top-performing model, achieving an F1-score of 0.99 in classifying attacks.
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