Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework
Abstract Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to de...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88135-9 |
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| author | Milos Antonijevic Miodrag Zivkovic Milica Djuric Jovicic Bosko Nikolic Jasmina Perisic Marina Milovanovic Luka Jovanovic Mahmoud Abdel-Salam Nebojsa Bacanin |
| author_facet | Milos Antonijevic Miodrag Zivkovic Milica Djuric Jovicic Bosko Nikolic Jasmina Perisic Marina Milovanovic Luka Jovanovic Mahmoud Abdel-Salam Nebojsa Bacanin |
| author_sort | Milos Antonijevic |
| collection | DOAJ |
| description | Abstract Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure. Conventional security techniques have difficulty countering these evolving threats, highlighting the need for adaptive solutions powered by artificial intelligence (AI). This work seeks to improve trust and security in IoT edge devices integrated in to the Metaverse. This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. A two-leveled architecture was designed to manage intricate data, enabling the detection and classification of attacks within IoT networks. A thorough analysis utilizing a real-world IoT network attacks dataset validates the proposed architecture’s efficacy in identification of the specific variants of malevolent assaults, that is a classic multi-class classification challenge. Three experiments were executed utilizing data open to public, where the top models attained a supreme accuracy of 99.83% for multi-class classification. Additionally, explainable AI methods offered valuable supplementary insights into the model’s decision-making process, supporting future data collection efforts and enhancing security of these systems. |
| format | Article |
| id | doaj-art-43de0713be54407488d7193d9397030b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-43de0713be54407488d7193d9397030b2025-02-02T12:17:07ZengNature PortfolioScientific Reports2045-23222025-01-0115113110.1038/s41598-025-88135-9Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level frameworkMilos Antonijevic0Miodrag Zivkovic1Milica Djuric Jovicic2Bosko Nikolic3Jasmina Perisic4Marina Milovanovic5Luka Jovanovic6Mahmoud Abdel-Salam7Nebojsa Bacanin8Singidunum UniversitySingidunum UniversityInnovation Centre, School of Electrical Engineering, University of BelgradeSchool of Electrical Engineering, University of BelgradeSingidunum UniversitySingidunum UniversitySingidunum UniversityFaculty of Computer and Information Science, Mansoura UniversitySingidunum UniversityAbstract Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure. Conventional security techniques have difficulty countering these evolving threats, highlighting the need for adaptive solutions powered by artificial intelligence (AI). This work seeks to improve trust and security in IoT edge devices integrated in to the Metaverse. This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. A two-leveled architecture was designed to manage intricate data, enabling the detection and classification of attacks within IoT networks. A thorough analysis utilizing a real-world IoT network attacks dataset validates the proposed architecture’s efficacy in identification of the specific variants of malevolent assaults, that is a classic multi-class classification challenge. Three experiments were executed utilizing data open to public, where the top models attained a supreme accuracy of 99.83% for multi-class classification. Additionally, explainable AI methods offered valuable supplementary insights into the model’s decision-making process, supporting future data collection efforts and enhancing security of these systems.https://doi.org/10.1038/s41598-025-88135-9MetaverseCatBoostLightGBMOptimizationMetaheuristics algorithmsChimp optimization algorithm |
| spellingShingle | Milos Antonijevic Miodrag Zivkovic Milica Djuric Jovicic Bosko Nikolic Jasmina Perisic Marina Milovanovic Luka Jovanovic Mahmoud Abdel-Salam Nebojsa Bacanin Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework Scientific Reports Metaverse CatBoost LightGBM Optimization Metaheuristics algorithms Chimp optimization algorithm |
| title | Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework |
| title_full | Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework |
| title_fullStr | Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework |
| title_full_unstemmed | Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework |
| title_short | Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework |
| title_sort | intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework |
| topic | Metaverse CatBoost LightGBM Optimization Metaheuristics algorithms Chimp optimization algorithm |
| url | https://doi.org/10.1038/s41598-025-88135-9 |
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