Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities
Abstract With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. Malicious agents regularly utilize novel technical vulnerabilities to use IoT networks in essential industries, the military, defence systems, a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88843-2 |
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| author | Mahmoud Ragab Ehab Bahaudien Ashary Bandar M. Alghamdi Rania Aboalela Naif Alsaadi Louai A. Maghrabi Khalid H. Allehaibi |
| author_facet | Mahmoud Ragab Ehab Bahaudien Ashary Bandar M. Alghamdi Rania Aboalela Naif Alsaadi Louai A. Maghrabi Khalid H. Allehaibi |
| author_sort | Mahmoud Ragab |
| collection | DOAJ |
| description | Abstract With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. Malicious agents regularly utilize novel technical vulnerabilities to use IoT networks in essential industries, the military, defence systems, and medical diagnosis. The IoT has enabled well-known connectivity by connecting many services and objects. However, it has additionally made cloud and IoT frameworks vulnerable to cyberattacks, production cybersecurity major concerns, mainly for the growth of trustworthy IoT networks, particularly those empowering smart city systems. Federated Learning (FL) offers an encouraging solution to address these challenges by providing a privacy-preserving solution for investigating and detecting cyberattacks in IoT systems without negotiating data privacy. Nevertheless, the possibility of FL regarding IoT forensics remains mostly unexplored. Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. This manuscript presents an Advanced Artificial Intelligence with a Federated Learning Framework for Privacy-Preserving Cyberthreat Detection (AAIFLF-PPCD) approach in IoT-assisted sustainable smart cities. The AAIFLF-PPCD approach aims to ensure robust and scalable cyberthreat detection while preserving the privacy of IoT users in smart cities. Initially, the AAIFLF-PPCD model utilizes Harris Hawk optimization (HHO)-based feature selection to identify the most related features from the IoT data. Next, the stacked sparse auto-encoder (SSAE) classifier is employed for detecting cyberthreats. Eventually, the walrus optimization algorithm (WOA) is used for hyperparameter tuning to improve the parameters of the SSAE approach and achieve optimal performance. The simulated outcome of the AAIFLF-PPCD technique is evaluated using a benchmark dataset. The performance validation of the AAIFLF-PPCD technique exhibited a superior accuracy value of 99.47% over existing models under diverse measures. |
| format | Article |
| id | doaj-art-4cd56422a9a44e0f81bd1c4562804731 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4cd56422a9a44e0f81bd1c45628047312025-02-09T12:33:47ZengNature PortfolioScientific Reports2045-23222025-02-0115112610.1038/s41598-025-88843-2Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart citiesMahmoud Ragab0Ehab Bahaudien Ashary1Bandar M. Alghamdi2Rania Aboalela3Naif Alsaadi4Louai A. Maghrabi5Khalid H. Allehaibi6Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz UniversityElectrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz UniversityInformation Technology Department, Faculty of Computing and Information Technology, King Abdulaziz UniversityInformation Systems Department, Faculty of Computing and Information Technology at Rabigh, King Abdulaziz UniversityDepartment of Industrial Engineering, Faculty of Engineering at Rabigh, King Abdulaziz UniversityDepartment of Software Engineering, College of Engineering, University of Business and TechnologyComputer Science Department, Faculty of Computing and Information Technology, King Abdulaziz UniversityAbstract With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. Malicious agents regularly utilize novel technical vulnerabilities to use IoT networks in essential industries, the military, defence systems, and medical diagnosis. The IoT has enabled well-known connectivity by connecting many services and objects. However, it has additionally made cloud and IoT frameworks vulnerable to cyberattacks, production cybersecurity major concerns, mainly for the growth of trustworthy IoT networks, particularly those empowering smart city systems. Federated Learning (FL) offers an encouraging solution to address these challenges by providing a privacy-preserving solution for investigating and detecting cyberattacks in IoT systems without negotiating data privacy. Nevertheless, the possibility of FL regarding IoT forensics remains mostly unexplored. Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. This manuscript presents an Advanced Artificial Intelligence with a Federated Learning Framework for Privacy-Preserving Cyberthreat Detection (AAIFLF-PPCD) approach in IoT-assisted sustainable smart cities. The AAIFLF-PPCD approach aims to ensure robust and scalable cyberthreat detection while preserving the privacy of IoT users in smart cities. Initially, the AAIFLF-PPCD model utilizes Harris Hawk optimization (HHO)-based feature selection to identify the most related features from the IoT data. Next, the stacked sparse auto-encoder (SSAE) classifier is employed for detecting cyberthreats. Eventually, the walrus optimization algorithm (WOA) is used for hyperparameter tuning to improve the parameters of the SSAE approach and achieve optimal performance. The simulated outcome of the AAIFLF-PPCD technique is evaluated using a benchmark dataset. The performance validation of the AAIFLF-PPCD technique exhibited a superior accuracy value of 99.47% over existing models under diverse measures.https://doi.org/10.1038/s41598-025-88843-2Federated LearningPrivacy preservingArtificial IntelligenceCyberthreatSmart citiesIoT |
| spellingShingle | Mahmoud Ragab Ehab Bahaudien Ashary Bandar M. Alghamdi Rania Aboalela Naif Alsaadi Louai A. Maghrabi Khalid H. Allehaibi Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities Scientific Reports Federated Learning Privacy preserving Artificial Intelligence Cyberthreat Smart cities IoT |
| title | Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities |
| title_full | Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities |
| title_fullStr | Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities |
| title_full_unstemmed | Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities |
| title_short | Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities |
| title_sort | advanced artificial intelligence with federated learning framework for privacy preserving cyberthreat detection in iot assisted sustainable smart cities |
| topic | Federated Learning Privacy preserving Artificial Intelligence Cyberthreat Smart cities IoT |
| url | https://doi.org/10.1038/s41598-025-88843-2 |
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