Analysis of different IDS-based machine learning models for secure data transmission in IoT networks
The Internet of Things (IoT) encompasses a network of interconnected devices that collect, analyze, and exchange vast amounts of data. However, this connectivity creates opportunities for various types of cyberattacks, making IoT systems vulnerable and potentially leading to the compromise of sensit...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-07-01
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| Series: | Open Computer Science |
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| Online Access: | https://doi.org/10.1515/comp-2025-0032 |
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| _version_ | 1849428976591699968 |
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| author | Gladić Dejana Petrovački Jelena Sladojević Srdan Arsenović Marko Ristić Sonja |
| author_facet | Gladić Dejana Petrovački Jelena Sladojević Srdan Arsenović Marko Ristić Sonja |
| author_sort | Gladić Dejana |
| collection | DOAJ |
| description | The Internet of Things (IoT) encompasses a network of interconnected devices that collect, analyze, and exchange vast amounts of data. However, this connectivity creates opportunities for various types of cyberattacks, making IoT systems vulnerable and potentially leading to the compromise of sensitive information. Therefore, developing effective intrusion detection system (IDS) is one of the key challenges in IoT network security. The aim of this study is to develop a machine learning (ML) model for network traffic classification and attack detection in IoT environments. Through a comparative analysis of different algorithms, the study seeks to identify the model with the best performance, which could serve as a foundation for efficient IDS solutions tailored to the specific characteristics of IoT networks. The RT-IoT2022 dataset was used for experimental analysis, providing realistic framework for testing ML models, including k-nearest neighbors, Random Forest, XGBoost, multilayer perceptron, and various 1D convolutional neural network architectures. The study examines preprocessing techniques, focusing on dimensionality reduction (principal component analysis, variance inflation factor, Pearson’s test), outlier detection (interquartile range, Z-score, Isolation Forest), and transformation methods (Box–Cox, RobustScaler, Winsorization). Based on the results of the experiment, the most effective model and preprocessing technique were proposed. |
| format | Article |
| id | doaj-art-e246fc2d5f114f7687a9b36ef65182d7 |
| institution | Kabale University |
| issn | 2299-1093 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Computer Science |
| spelling | doaj-art-e246fc2d5f114f7687a9b36ef65182d72025-08-20T03:28:29ZengDe GruyterOpen Computer Science2299-10932025-07-01151p. 36013610.1515/comp-2025-0032Analysis of different IDS-based machine learning models for secure data transmission in IoT networksGladić Dejana0Petrovački Jelena1Sladojević Srdan2Arsenović Marko3Ristić Sonja4Faculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Novi Sad, SerbiaThe Internet of Things (IoT) encompasses a network of interconnected devices that collect, analyze, and exchange vast amounts of data. However, this connectivity creates opportunities for various types of cyberattacks, making IoT systems vulnerable and potentially leading to the compromise of sensitive information. Therefore, developing effective intrusion detection system (IDS) is one of the key challenges in IoT network security. The aim of this study is to develop a machine learning (ML) model for network traffic classification and attack detection in IoT environments. Through a comparative analysis of different algorithms, the study seeks to identify the model with the best performance, which could serve as a foundation for efficient IDS solutions tailored to the specific characteristics of IoT networks. The RT-IoT2022 dataset was used for experimental analysis, providing realistic framework for testing ML models, including k-nearest neighbors, Random Forest, XGBoost, multilayer perceptron, and various 1D convolutional neural network architectures. The study examines preprocessing techniques, focusing on dimensionality reduction (principal component analysis, variance inflation factor, Pearson’s test), outlier detection (interquartile range, Z-score, Isolation Forest), and transformation methods (Box–Cox, RobustScaler, Winsorization). Based on the results of the experiment, the most effective model and preprocessing technique were proposed.https://doi.org/10.1515/comp-2025-0032internet of thingsintrusion detection systemmachine learning modelclassificationcnn |
| spellingShingle | Gladić Dejana Petrovački Jelena Sladojević Srdan Arsenović Marko Ristić Sonja Analysis of different IDS-based machine learning models for secure data transmission in IoT networks Open Computer Science internet of things intrusion detection system machine learning model classification cnn |
| title | Analysis of different IDS-based machine learning models for secure data transmission in IoT networks |
| title_full | Analysis of different IDS-based machine learning models for secure data transmission in IoT networks |
| title_fullStr | Analysis of different IDS-based machine learning models for secure data transmission in IoT networks |
| title_full_unstemmed | Analysis of different IDS-based machine learning models for secure data transmission in IoT networks |
| title_short | Analysis of different IDS-based machine learning models for secure data transmission in IoT networks |
| title_sort | analysis of different ids based machine learning models for secure data transmission in iot networks |
| topic | internet of things intrusion detection system machine learning model classification cnn |
| url | https://doi.org/10.1515/comp-2025-0032 |
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