Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT)
To combat the growing danger of zero-day attacks on IoT networks, this study introduces a Cluster-Based Classification (CBC) method. Security vulnerabilities have become more apparent with the growth of IoT devices, calling for new approaches to identify unique threats quickly. The hybrid CBC approa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/6/251 |
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| _version_ | 1849431872944209920 |
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| author | Naveed Ahmed Md Asri Ngadi Abdulaleem Ali Almazroi Nouf Atiahallah Alghanmi |
| author_facet | Naveed Ahmed Md Asri Ngadi Abdulaleem Ali Almazroi Nouf Atiahallah Alghanmi |
| author_sort | Naveed Ahmed |
| collection | DOAJ |
| description | To combat the growing danger of zero-day attacks on IoT networks, this study introduces a Cluster-Based Classification (CBC) method. Security vulnerabilities have become more apparent with the growth of IoT devices, calling for new approaches to identify unique threats quickly. The hybrid CBC approach uses optimized k-means clustering to find commonalities across different abnormalities, intending to quickly identify and classify unknown harmful attacks in a varied IoT network. The technique is fine-tuned for eight-class and two-class classifications, supporting different attacks using the IoTCIC2023 dataset and SelectKBest feature selection. Robust analysis is achieved by evaluating and aggregating the performance of machine learning classifiers such as XGBoost, AdaBoost, KNN, and Random Forest. In two-class classification, Random Forest achieves 95.11% accuracy, while in eight-class classification, KNN tops the charts with 88.24%. These results demonstrate noteworthy accuracy. The suggested CBC technique is effective, as shown by comparisons with state-of-the-art approaches. Despite several caveats and dataset specifications, this study provides a useful tool for academics and practitioners in the ever-changing field of cybersecurity by suggesting a method to strengthen the security of IoT networks against new threats. |
| format | Article |
| id | doaj-art-e6820dbab8b84e4f870bb1ade9e4edfd |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-e6820dbab8b84e4f870bb1ade9e4edfd2025-08-20T03:27:29ZengMDPI AGFuture Internet1999-59032025-05-0117625110.3390/fi17060251Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT)Naveed Ahmed0Md Asri Ngadi1Abdulaleem Ali Almazroi2Nouf Atiahallah Alghanmi3Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaDepartment of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Rabigh 21911, Saudi ArabiaDepartment of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Rabigh 21911, Saudi ArabiaTo combat the growing danger of zero-day attacks on IoT networks, this study introduces a Cluster-Based Classification (CBC) method. Security vulnerabilities have become more apparent with the growth of IoT devices, calling for new approaches to identify unique threats quickly. The hybrid CBC approach uses optimized k-means clustering to find commonalities across different abnormalities, intending to quickly identify and classify unknown harmful attacks in a varied IoT network. The technique is fine-tuned for eight-class and two-class classifications, supporting different attacks using the IoTCIC2023 dataset and SelectKBest feature selection. Robust analysis is achieved by evaluating and aggregating the performance of machine learning classifiers such as XGBoost, AdaBoost, KNN, and Random Forest. In two-class classification, Random Forest achieves 95.11% accuracy, while in eight-class classification, KNN tops the charts with 88.24%. These results demonstrate noteworthy accuracy. The suggested CBC technique is effective, as shown by comparisons with state-of-the-art approaches. Despite several caveats and dataset specifications, this study provides a useful tool for academics and practitioners in the ever-changing field of cybersecurity by suggesting a method to strengthen the security of IoT networks against new threats.https://www.mdpi.com/1999-5903/17/6/251IoT securitynovel attack detectioncluster-based classification |
| spellingShingle | Naveed Ahmed Md Asri Ngadi Abdulaleem Ali Almazroi Nouf Atiahallah Alghanmi Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT) Future Internet IoT security novel attack detection cluster-based classification |
| title | Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT) |
| title_full | Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT) |
| title_fullStr | Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT) |
| title_full_unstemmed | Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT) |
| title_short | Hybrid Model for Novel Attack Detection Using a Cluster-Based Machine Learning Classification Approach for the Internet of Things (IoT) |
| title_sort | hybrid model for novel attack detection using a cluster based machine learning classification approach for the internet of things iot |
| topic | IoT security novel attack detection cluster-based classification |
| url | https://www.mdpi.com/1999-5903/17/6/251 |
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