Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment
Abstract In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics aut...
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| Format: | Article |
| Language: | English |
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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/s41598-025-87454-1 |
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| author | Fatma S. Alrayes Mohammed Maray Asma Alshuhail Khaled Mohamad Almustafa Abdulbasit A. Darem Ali M. Al-Sharafi Shoayee Dlaim Alotaibi |
| author_facet | Fatma S. Alrayes Mohammed Maray Asma Alshuhail Khaled Mohamad Almustafa Abdulbasit A. Darem Ali M. Al-Sharafi Shoayee Dlaim Alotaibi |
| author_sort | Fatma S. Alrayes |
| collection | DOAJ |
| description | Abstract In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications. Privacy-preserving machine learning (ML) training in the development of aggregation permits a demander to firmly train ML techniques with the delicate data of IoT collected from IoT devices. The current solution is primarily server-assisted and fails to address collusion attacks among servers or data owners. Additionally, it needs to adequately account for the complex dynamics of the IoT environment. In a large-sized big data environment, privacy protection challenges are additionally enlarged. The data dimensional can have vague meaningful patterns, making it challenging to certify that privacy-preserving models do not destroy the efficacy and accuracy of statistical methods. This manuscript presents a Privacy-Preserving Statistical Learning with an Optimization Algorithm for a High-Dimensional Big Data Environment (PPSLOA-HDBDE) approach. The primary purpose of the PPSLOA-HDBDE approach is to utilize advanced optimization and ensemble techniques to ensure data confidentiality while maintaining analytical efficacy. In the primary stage, the linear scaling normalization (LSN) method scales the input data. Besides, the sand cat swarm optimizer (SCSO)-based feature selection (FS) process is employed to decrease the high dimensionality problem. Moreover, the recognition of intrusion detection takes place by using an ensemble of temporal convolutional network (TCN), multi-layer auto-encoder (MAE), and extreme gradient boosting (XGBoost) models. Lastly, the hyperparameter tuning of the three models is accomplished by utilizing an improved marine predator algorithm (IMPA) method. An extensive range of experimentations is performed to improve the PPSLOA-HDBDE technique’s performance, and the outcomes are examined under distinct measures. The performance validation of the PPSLOA-HDBDE technique illustrated a superior accuracy value of 99.49% over existing models. |
| format | Article |
| id | doaj-art-ab965ef941f14bd482bdbca419c400fa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ab965ef941f14bd482bdbca419c400fa2025-02-02T12:20:48ZengNature PortfolioScientific Reports2045-23222025-01-0115112710.1038/s41598-025-87454-1Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environmentFatma S. Alrayes0Mohammed Maray1Asma Alshuhail2Khaled Mohamad Almustafa3Abdulbasit A. Darem4Ali M. Al-Sharafi5Shoayee Dlaim Alotaibi6Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Information Systems, College of Computer Science, King Khalid UniversityDepartment of Information Systems, College of Computer Sciences & Information Technology, King Faisal UniversityDepartment of Electrical and Computer Engineering, Gulf University for Science and Technology (GUST)Center for Scientific Research and Entrepreneurship, Northern Border UniversityDepartment of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of BishaDepartment of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of HailAbstract In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications. Privacy-preserving machine learning (ML) training in the development of aggregation permits a demander to firmly train ML techniques with the delicate data of IoT collected from IoT devices. The current solution is primarily server-assisted and fails to address collusion attacks among servers or data owners. Additionally, it needs to adequately account for the complex dynamics of the IoT environment. In a large-sized big data environment, privacy protection challenges are additionally enlarged. The data dimensional can have vague meaningful patterns, making it challenging to certify that privacy-preserving models do not destroy the efficacy and accuracy of statistical methods. This manuscript presents a Privacy-Preserving Statistical Learning with an Optimization Algorithm for a High-Dimensional Big Data Environment (PPSLOA-HDBDE) approach. The primary purpose of the PPSLOA-HDBDE approach is to utilize advanced optimization and ensemble techniques to ensure data confidentiality while maintaining analytical efficacy. In the primary stage, the linear scaling normalization (LSN) method scales the input data. Besides, the sand cat swarm optimizer (SCSO)-based feature selection (FS) process is employed to decrease the high dimensionality problem. Moreover, the recognition of intrusion detection takes place by using an ensemble of temporal convolutional network (TCN), multi-layer auto-encoder (MAE), and extreme gradient boosting (XGBoost) models. Lastly, the hyperparameter tuning of the three models is accomplished by utilizing an improved marine predator algorithm (IMPA) method. An extensive range of experimentations is performed to improve the PPSLOA-HDBDE technique’s performance, and the outcomes are examined under distinct measures. The performance validation of the PPSLOA-HDBDE technique illustrated a superior accuracy value of 99.49% over existing models.https://doi.org/10.1038/s41598-025-87454-1Privacy-preservingEnsemble modelLinear scaling normalizationHigh-dimensionalBig dataIntrusion detection |
| spellingShingle | Fatma S. Alrayes Mohammed Maray Asma Alshuhail Khaled Mohamad Almustafa Abdulbasit A. Darem Ali M. Al-Sharafi Shoayee Dlaim Alotaibi Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment Scientific Reports Privacy-preserving Ensemble model Linear scaling normalization High-dimensional Big data Intrusion detection |
| title | Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment |
| title_full | Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment |
| title_fullStr | Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment |
| title_full_unstemmed | Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment |
| title_short | Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment |
| title_sort | privacy preserving approach for iot networks using statistical learning with optimization algorithm on high dimensional big data environment |
| topic | Privacy-preserving Ensemble model Linear scaling normalization High-dimensional Big data Intrusion detection |
| url | https://doi.org/10.1038/s41598-025-87454-1 |
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