ENSEMBLE MACHINE LEARNING APPROACH FOR IOT INTRUSION DETECTION SYSTEMS - A research-based IoT security proposal

Aug 4, 2026

The rapid expansion of the Internet of Things (IoT) has driven advances across smart cities, healthcare, automotive, and logistics, while simultaneously increasing security risks from evolving network threats. The research develops an intelligent anomaly-based network intrusion detection system (NIDS) using ensemble machine learning (ML) algorithms. It evaluates logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting on three intrusion detection datasets. The ensemble ML approach achieves high accuracy across UNSW-NB15, IoTID20, and BoTNeTIoT-L01-v2.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Related work

IoT intrusion detection system

Ensemble machine learning

Classifiers

Proposed method

Experimental results

Conclusion

| | | Iraqi Journal for Computers and Informatics | Vol. [ 49 ], Issue [2 ], Year (2023 ) | | | | --- | --- | --- | --- | --- | --- | ENSEMBLE MACHINE LEARNING APPROACH FOR IOT INTRUSION DETECTION SYSTEMS Baseem A. Kadheem Hammood 1 1Department of Computer Science Informatics Institute for Postgraduate Studies, Iraqi Commission for Computers and Informatics Baghdad, Iraq [ms202130656@iips.icci.edu.iq](ms202130656@iips.icci.edu.iq) Ahmed T. Sadiq2 2Department of Computer Science University of Technology Baghdad, Iraq Ahmed.T.Sadiq@uotechnology.rdu.iq Abstract-The rapid growth and development of the Internet of Things (IoT) have had an important impact on various industries, including smart cities, the medical profession, autos, and logistics tracking. However, with the benefits of the IoT come security concerns that are becoming increasingly prevalent. This issue is being addressed by developing intelligent network intrusion detection systems (NIDS) using machine learning (ML) techniques to detect constantly changing network threats and patterns. Ensemble ML represents the recent direction in the ML field. This research proposes a new anomaly-based solution for IoT networks utilizing ensemble ML algorithms, including logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting. The algorithms were tested on three different intrusion detection datasets. The ensemble ML method achieved an accuracy of 98.52% when applied to the UNSW-NB15 dataset, 88.41% on the IoTID20 dataset, and 91.03% on the BoTNeTIoT-L01-v2 dataset. Index Terms - intrusion detection system, Machine Learning, IoT, Ensemble. I. INTRODUCTION Discovering emerging and unknown attacks requires an approach that can detect Internet of Things (IoT) intrusion; machine learning (ML) possesses this ability [1] . The rapid growth of cyberattacks has resulted in the need of IoT’s security architecture for intrusion detection. The security field faces serious challenges in the development of technology and the IoT. Current security methods do not provide adequate protection; hence, cyberattacks are increasing. [2] . With the use of an ML-based approach, an intrusion detection system (IDS) was proposed for use on the IoT. The proposed model can be trained on different sources from large and classified datasets. This model can work effectively after being trained on smaller-sized data and classifying them in the target domain [3] . Another IoT IDS has been proposed using ML and enhanced transient search optimization. The proposed system uses an enhanced transient search optimization algorithm to optimize the hyperparameters of the ML model. The outcomes of this paper show that the recommended system outperforms other IDS in terms of accuracy and false alarm rate [4] . This work uses ensemble ML methods to detect intrusion in IoT networks. This article is organized as follows: Section 2 presents the related work, Section 3 presents the IoT intrusion detection system, Section 4 introduces ensemble ML, Section 5 provides the classifiers, Section 6 presents the proposed method, Sections 7 and 8 detail the experimental results, and Section 9 concludes this paper. II. RELATED WORK In this section, some previous works in the field of IoTIDS are reviewed. In [5], feature sets were used, and ML methods using multiple over-cluster approaches (artificial neural networks (NN), backing machines, and random forests (RF), and message queue telemetry transport (MQTT), a transport metric for waiting messages, UNSW-NB15, which is feature-based by TCP. The best features in the two groups were obtained, with high accuracy and less time for the ML algorithms. RF, binary, and the use of radio frequency on stream data and MQTT achieved accuracies of 97.37%, 98.67%, and 97.54%, respectively. In [6], four algorithms—naive Bayes (NB), RF, J48, and zero—were utilized to categorize cyberattacks on the UNSWNB15 dataset. T

FAQs

What problem does the proposed approach address?

It targets intrusion detection in IoT networks by handling constantly changing threats and patterns using machine learning-based NIDS.

Which ensemble ML algorithms are used in the study?

The research tests logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting within an ensemble ML framework.

How is performance evaluated and what results are reported?

The algorithms are tested on UNSW-NB15, IoTID20, and BoTNeTIoT-L01-v2 datasets, achieving accuracy of 98.52%, 88.41%, and 91.03% respectively.

Aug 4, 2026

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