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

2026-06-30 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Internet of Things (IoT) & Connected Devices · Depth: Advanced, quick

Summary

A comparative analysis of machine learning-based intrusion detection systems (IDS) for rapidly expanding Internet of Things (IoT) networks was conducted using the Gotham2025 dataset. This dataset, generated from the Gotham testbed, simulates 78 diverse IoT devices utilizing protocols such as MQTT, CoAP, and RTSP, addressing critical security and privacy challenges in resource-limited IoT environments. The study evaluated five machine learning algorithms: Random Forest, XGBoost, Logistic Regression, Naive Bayes, and Deep Neural Network. Results demonstrated that the Random Forest Classifier emerged as the top-performing model, achieving an F1-score of 0.99 in accurately classifying various attacks, thereby offering a robust solution for enhancing IoT network security.

Key takeaway

For AI Security Engineers designing intrusion detection systems for IoT networks, this research indicates that Random Forest is a highly effective algorithm. You should prioritize evaluating Random Forest for its proven 0.99 F1-score performance in classifying attacks within resource-constrained IoT environments. Consider using datasets like Gotham2025 for robust testing and validation of your IDS solutions to ensure comprehensive protection against evolving threats.

Key insights

Random Forest excels in IoT intrusion detection, achieving a 0.99 F1-score on the Gotham2025 dataset.

Principles

• IoT security requires resource-aware IDS.
• ML can effectively classify IoT network attacks.
• Comparative analysis identifies optimal algorithms.

Method

Five ML algorithms (Random Forest, XGBoost, Logistic Regression, Naive Bayes, Deep Neural Network) were compared on the Gotham2025 dataset, generated from 78 emulated IoT devices, to classify network intrusions.

In practice

• Deploy Random Forest for IoT IDS.
• Utilize Gotham2025 for IDS benchmarking.
• Emulate diverse IoT protocols for testing.

Topics

• IoT Security
• Intrusion Detection Systems
• Machine Learning
• Random Forest
• Gotham2025 Dataset
• Network Protocols

Best for: AI Engineer, Research Scientist, AI Security Engineer, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.