Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework
Abstract Cybersecurity has been defined as a vital part of the developments, which is mainly related to technology. Enlarged cybersecurity safeguards that the data remains safe. Cyberattacks like computer malware, denial-of-service (DoS) attacks, or unauthorized access led to severe damage and econo...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99452-4 |
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| author | Manal Abdullah Alohali Hatim Dafaalla Mohammed Baihan Sultan Alahmari Achraf Ben Miled Othman Alrusaini Ali Alqazzaz Hanadi Alkhudhayr |
| author_facet | Manal Abdullah Alohali Hatim Dafaalla Mohammed Baihan Sultan Alahmari Achraf Ben Miled Othman Alrusaini Ali Alqazzaz Hanadi Alkhudhayr |
| author_sort | Manal Abdullah Alohali |
| collection | DOAJ |
| description | Abstract Cybersecurity has been defined as a vital part of the developments, which is mainly related to technology. Enlarged cybersecurity safeguards that the data remains safe. Cyberattacks like computer malware, denial-of-service (DoS) attacks, or unauthorized access led to severe damage and economic losses in large-scale systems. Cybersecurity includes decreasing the risk of mischievous computer, software, and network attacks. Novel techniques have been combined into emerging artificial intelligence (AI) that attains cybersecurity. ML is usually reflected as a sub-branch of AI, which is closely linked to data mining, computational statistics, and data science (DS) and mainly concentrates on generating computers to acquire data. Federated learning (FL) is one of the ML models that permits tackling cyberattack issues like security, data privacy, and access rights. This study proposes a Self-Attention Mechanism-Driven Federated Learning for Secure Cyberattack Detection with Crocodile Optimization Algorithm (SAMFL-SCDCOA) methodology. The main objective of the SAMFL-SCDCOA methodology is to provide an effective method for preventing cyberattacks in real time using FL and advanced optimization algorithms. Initially, the Z-score normalization is utilized to scale and standardize data to improve analysis consistency and accuracy. Furthermore, the feature selection (FS) process uses the crocodile optimization algorithm (COA) model. The proposed SAMFL-SCDCOA approach employs the gated recurrent unit with a self-attention (GRU-SA) model for the cybersecurity classification. Finally, the improved pelican optimization algorithm (IPOA) optimally adjusts the hyperparameter values of the GRU-SA model, leading to enhanced classification performance. A wide range of experiments has been accomplished to validate the performance of the SAMFL-SCDCOA technique under the CICIDS-2017 dataset. The comparison study of the SAMFL-SCDCOA technique emphasized a superior output of 99.04% over existing models. |
| format | Article |
| id | doaj-art-a9b11c0293cd43199cbf72a4c2d6e591 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a9b11c0293cd43199cbf72a4c2d6e5912025-08-20T03:38:16ZengNature PortfolioScientific Reports2045-23222025-07-0115112310.1038/s41598-025-99452-4Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning frameworkManal Abdullah Alohali0Hatim Dafaalla1Mohammed Baihan2Sultan Alahmari3Achraf Ben Miled4Othman Alrusaini5Ali Alqazzaz6Hanadi Alkhudhayr7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman UniversityDepartment of Computer Science, Applied College at Mahayil, King Khalid UniversityDepartment of Computer Science, Community College, King Saud UniversityKing Abdul Aziz City for Science and Technology (KACST), Cybersecurity InstituteDepartment of Computer Science, College of Science, Northern Border UniversityDepartment of Engineering and Applied Sciences, Applied College, Umm Al-Qura University Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of BishaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz UniversityAbstract Cybersecurity has been defined as a vital part of the developments, which is mainly related to technology. Enlarged cybersecurity safeguards that the data remains safe. Cyberattacks like computer malware, denial-of-service (DoS) attacks, or unauthorized access led to severe damage and economic losses in large-scale systems. Cybersecurity includes decreasing the risk of mischievous computer, software, and network attacks. Novel techniques have been combined into emerging artificial intelligence (AI) that attains cybersecurity. ML is usually reflected as a sub-branch of AI, which is closely linked to data mining, computational statistics, and data science (DS) and mainly concentrates on generating computers to acquire data. Federated learning (FL) is one of the ML models that permits tackling cyberattack issues like security, data privacy, and access rights. This study proposes a Self-Attention Mechanism-Driven Federated Learning for Secure Cyberattack Detection with Crocodile Optimization Algorithm (SAMFL-SCDCOA) methodology. The main objective of the SAMFL-SCDCOA methodology is to provide an effective method for preventing cyberattacks in real time using FL and advanced optimization algorithms. Initially, the Z-score normalization is utilized to scale and standardize data to improve analysis consistency and accuracy. Furthermore, the feature selection (FS) process uses the crocodile optimization algorithm (COA) model. The proposed SAMFL-SCDCOA approach employs the gated recurrent unit with a self-attention (GRU-SA) model for the cybersecurity classification. Finally, the improved pelican optimization algorithm (IPOA) optimally adjusts the hyperparameter values of the GRU-SA model, leading to enhanced classification performance. A wide range of experiments has been accomplished to validate the performance of the SAMFL-SCDCOA technique under the CICIDS-2017 dataset. The comparison study of the SAMFL-SCDCOA technique emphasized a superior output of 99.04% over existing models.https://doi.org/10.1038/s41598-025-99452-4Self-attention mechanismFederated learningCyberattack detectionCrocodile optimization algorithmCybersecurity |
| spellingShingle | Manal Abdullah Alohali Hatim Dafaalla Mohammed Baihan Sultan Alahmari Achraf Ben Miled Othman Alrusaini Ali Alqazzaz Hanadi Alkhudhayr Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework Scientific Reports Self-attention mechanism Federated learning Cyberattack detection Crocodile optimization algorithm Cybersecurity |
| title | Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework |
| title_full | Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework |
| title_fullStr | Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework |
| title_full_unstemmed | Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework |
| title_short | Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework |
| title_sort | leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework |
| topic | Self-attention mechanism Federated learning Cyberattack detection Crocodile optimization algorithm Cybersecurity |
| url | https://doi.org/10.1038/s41598-025-99452-4 |
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