Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networks

2026-06-30

AI Summary

This study addresses the urgent need for efficient and accurate intrusion detection mechanisms in resource-constrained IoT devices. Leveraging the realistic Gotham2025 network dataset, which encompasses traffic from 78 emulated IoT devices, the authors present the first systematic evaluation of five mainstream machine learning models—Random Forest, XGBoost, Logistic Regression, Naive Bayes, and deep neural networks—on a high-fidelity testbed integrating multiple protocols including MQTT, CoAP, and RTSP. Experimental results demonstrate that Random Forest achieves superior performance, attaining an F1-score of 0.99 for attack classification, significantly outperforming all other evaluated methods. These findings establish Random Forest as a highly effective and lightweight solution for securing IoT environments under stringent computational constraints.

Abstract

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.

Problem

• IoT security
• intrusion detection
• resource-constrained devices
• privacy challenges
• network attacks

Innovation

• IoT security
• intrusion detection
• machine learning comparison
• Gotham2025 dataset
• Random Forest