Leveraging Graph Neural Networks for IoT Attack Detection

Onur Ceran; Erdal Özdoğan; Mevlüt Uysal

doi:10.35377/saucis...1663435

EN

Leveraging Graph Neural Networks for IoT Attack Detection

Abstract

The widespread adoption of Internet of Things (IoT) devices in multiple sectors has driven technological progress; however, it has simultaneously rendered networks vulnerable to advanced cyber threats. Conventional intrusion detection systems face challenges adjusting to IoT environments' ever-changing and diverse characteristics. To address this challenge, researchers propose a novel hybrid approach combining Graph Neural Networks and XGBoost algorithm for robust intrusion detection in IoT ecosystems. This paper presents a comprehensive methodology for integrating GNNs and XGBoost in IoT intrusion detection and evaluates its effectiveness using diverse datasets. The proposed model preprocesses data by standardization, handling missing values, and encoding categorical features. It leverages GNNs to model spatial dependencies and interactions within IoT networks and utilizes XGBoost to distill complex features for predictive analysis. The late fusion technique combines predictions from both models to enhance overall performance. Experimental results on four datasets, including CICIoT-2023, CICIDS-2017, UNSW-NB15, and IoMT-2024, demonstrate the efficacy of the hybrid model. High accuracy, precision, recall, and AUC values indicate the model's robustness in detecting attacks while minimizing false alarms. The study advances IoT security by introducing synergistic solutions and provides practical insights for implementing intrusion detection systems in real-world IoT environments.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Onur Ceran ^*
0000-0003-2147-0506
Türkiye

Erdal Özdoğan
0000-0002-3339-0493
Türkiye

Mevlüt Uysal
0000-0002-6934-4421
Türkiye

Early Pub Date

June 16, 2025

Publication Date

June 30, 2025

Submission Date

March 22, 2025

Acceptance Date

May 28, 2025

Published in Issue

Year 2025 Volume: 8 Number: 2

DOI

https://doi.org/10.35377/saucis...1663435

IZ

https://izlik.org/JA79GN68BH

Cite

RIS / Bibtex

APA

Ceran, O., Özdoğan, E., & Uysal, M. (2025). Leveraging Graph Neural Networks for IoT Attack Detection. Sakarya University Journal of Computer and Information Sciences, 8(2), 223-244. https://doi.org/10.35377/saucis...1663435

AMA

1.Ceran O, Özdoğan E, Uysal M. Leveraging Graph Neural Networks for IoT Attack Detection. SAUCIS. 2025;8(2):223-244. doi:10.35377/saucis.1663435

Chicago

Ceran, Onur, Erdal Özdoğan, and Mevlüt Uysal. 2025. “Leveraging Graph Neural Networks for IoT Attack Detection”. Sakarya University Journal of Computer and Information Sciences 8 (2): 223-44. https://doi.org/10.35377/saucis. 1663435.

EndNote

Ceran O, Özdoğan E, Uysal M (June 1, 2025) Leveraging Graph Neural Networks for IoT Attack Detection. Sakarya University Journal of Computer and Information Sciences 8 2 223–244.

IEEE

[1]O. Ceran, E. Özdoğan, and M. Uysal, “Leveraging Graph Neural Networks for IoT Attack Detection”, SAUCIS, vol. 8, no. 2, pp. 223–244, June 2025, doi: 10.35377/saucis...1663435.

ISNAD

Ceran, Onur - Özdoğan, Erdal - Uysal, Mevlüt. “Leveraging Graph Neural Networks for IoT Attack Detection”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 1, 2025): 223-244. https://doi.org/10.35377/saucis. 1663435.

JAMA

1.Ceran O, Özdoğan E, Uysal M. Leveraging Graph Neural Networks for IoT Attack Detection. SAUCIS. 2025;8:223–244.

MLA

Ceran, Onur, et al. “Leveraging Graph Neural Networks for IoT Attack Detection”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, June 2025, pp. 223-44, doi:10.35377/saucis. 1663435.

Vancouver

1.Onur Ceran, Erdal Özdoğan, Mevlüt Uysal. Leveraging Graph Neural Networks for IoT Attack Detection. SAUCIS. 2025 Jun. 1;8(2):223-44. doi:10.35377/saucis. 1663435