An Ensemble Approach for Detecting Network Attacks in IoT Environments

Alfathe, Mahmood

doi:10.69513/jncs.v2.i2.a9

	An Ensemble Approach for Detecting Network Attacks in IoT Environments
Al-Noor Journal for Information Technology and Cybersecurity
Article 9, Volume 2, Issue 2, December 2025, Pages 63-68 PDF (425.89 K)
Document Type: Research paper
DOI: 10.69513/jncs.v2.i2.a9
Author
mahmood alfathe^*
Ninevah university
Abstract
The Internet of Things (IoT) were declared to be the largest and connected network comprising millions of devices aimed at efficiency, automation, and better decision-making; hence it has been popularly branded "the fourth industrial revolution." But with the arrival of many IoT systems, their vulnerability to cyberattacks is also growing, thereby putting the connected devices and networks in severe compromised positions. This paper investigates the opportunity of using machine learning (ML) and ensemble techniques for enhancing cyber-attack detection in IoT environments. Six machine learning algorithms, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting, and Naive Bayes, were evaluated for detecting attacks in IoT network traffic. The ensemble comprised of the three models with the best performance combined in a soft-voting manner so that the complementary strengths were exploited, hence improving robustness and generalization. The performance of the ensemble was measured using accuracy, precision, recall, F1-score, and the area under the Receiver Operating Characteristic curve. The proposed ensemble shows a test accuracy of 99.91%, demonstrating its capacity to detect cyber threats effectively and the promise of ensemble learning schemes in securing cosmopolitan IoT infrastructures
Keywords
Cyber-attacks; Intrusion Detection System (IDS); Internet of Things (IoT); Machine learning; Ensemble Learning

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