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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2026 / Cilt: 11 Sayı: 2

A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM

İlhan Fırat Kılınçer(FIRAT ÜNİVERSİTESİ)0000-0001-8090-4998
Oğuzhan Katar(FIRAT ÜNİVERSİTESİ)0000-0002-5628-3543
Pages
321–328
Publication Date
2023-06-23
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It has been determined that the proposed method is effective in preventing cyber attacks on IoT/IIoT networks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The Internet of Things (IoT) is one of the technologies used in many fields today.
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Key Findings & Data:The obtained results were compared with the literature and it was seen that the proposed method provided the highest classification performance in the literature.
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Conclusion & Impact:It has been determined that the proposed method is effective in preventing cyber attacks on IoT/IIoT networks.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Internet of Things · Light GBM · ToN_IoT

Abstract

The Internet of Things (IoT) is one of the technologies used in many fields today. Cyber attacks against IoT/Industrial IoT (IIoT) networks, which are increasingly used thanks to the convenience it provides, are constantly increasing. Detection of attacks against IoT/IIoT networks is one of the popular topics recently. The development of a dataset for IoT applications is essential for the intrusion detection in IoT networks. In this context, the ToN_IoT dataset created in the laboratory of UNSW Canberra (Australia) is one of the most comprehensive datasets that can be used to detect cyber attacks on IoT networks. In this study, fridge, garage door, GPS tracker, modbus, motion light, weather, thermostat datasets related to IoT sensors from ToN_IoT datasets were used. The datasets used were subjected to multi-class classification with the Light Gradient Boosting Machine (LGBM) classifier proposed in the study. The obtained results were compared with the literature and it was seen that the proposed method provided the highest classification performance in the literature. It has been determined that the proposed method is effective in preventing cyber attacks on IoT/IIoT networks.

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Cite This Article

Kılınçer, İ. F., Katar, O. (2023). A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 11 Sayı: 2, 321–328. https://doi.org/10.29109/gujsc.1173286

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