A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM
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 popu...
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| Format: | Article |
| Language: | English |
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Gazi University
2023-06-01
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| Series: | Gazi Üniversitesi Fen Bilimleri Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/pub/gujsc/issue/78178/1173286 |
| _version_ | 1797685910845784064 |
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| author | İlhan Fırat KILINÇER Oğuzhan KATAR |
| author_facet | İlhan Fırat KILINÇER Oğuzhan KATAR |
| author_sort | İlhan Fırat KILINÇER |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:57:13Z |
| format | Article |
| id | doaj.art-6920846377d54754b37f7855c19753ab |
| institution | Directory Open Access Journal |
| issn | 2147-9526 |
| language | English |
| last_indexed | 2024-03-12T00:57:13Z |
| publishDate | 2023-06-01 |
| publisher | Gazi University |
| record_format | Article |
| series | Gazi Üniversitesi Fen Bilimleri Dergisi |
| spelling | doaj.art-6920846377d54754b37f7855c19753ab2023-09-14T12:32:57ZengGazi UniversityGazi Üniversitesi Fen Bilimleri Dergisi2147-95262023-06-0111232132810.29109/gujsc.1173286 A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBMİlhan Fırat KILINÇER0https://orcid.org/0000-0001-8090-4998Oğuzhan KATAR1https://orcid.org/0000-0002-5628-3543.FIRAT ÜNİVERSİTESİThe 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.https://dergipark.org.tr/tr/pub/gujsc/issue/78178/1173286internet of thingslight gbmton_iotcyber security |
| spellingShingle | İlhan Fırat KILINÇER Oğuzhan KATAR A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM Gazi Üniversitesi Fen Bilimleri Dergisi internet of things light gbm ton_iot cyber security |
| title | A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM |
| title_full | A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM |
| title_fullStr | A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM |
| title_full_unstemmed | A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM |
| title_short | A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM |
| title_sort | new intrusion detection system for secured iot iiot networks based on lgbm |
| topic | internet of things light gbm ton_iot cyber security |
| url | https://dergipark.org.tr/tr/pub/gujsc/issue/78178/1173286 |
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