CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
The implementation of comprehensive security measures is a critical factor in the rapid growth of industrial control networks. Federated Learning has emerged as a viable solution for safeguarding privacy in machine learning. The effectiveness of pattern detection in models is diminished as a result...
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2416 |
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| author | Shu Feng Luhan Gao Leyi Shi |
| author_facet | Shu Feng Luhan Gao Leyi Shi |
| author_sort | Shu Feng |
| collection | DOAJ |
| description | The implementation of comprehensive security measures is a critical factor in the rapid growth of industrial control networks. Federated Learning has emerged as a viable solution for safeguarding privacy in machine learning. The effectiveness of pattern detection in models is diminished as a result of the difficulty in extracting attack information from extremely large datasets and obtaining an adequate number of examples for specific types of attacks. A robust Federated Learning method, CGFL, is introduced in this study to resolve the challenges presented by data distribution discrepancies and client class imbalance. By employing a data generation strategy to generate balanced datasets for each client, CGFL enhances the global model. It employs a data generator that integrates artificially generated data with the existing data from local clients by employing label correction and data generation techniques. The geometric median aggregation technique was implemented to enhance the security of the aggregation process. The model was simulated and evaluated using the CIC-IDS2017 dataset, NSL-KDD dataset, and CSE-CIC-IDS2018 dataset. The experimental results indicate that CGFL does an effective job of enhancing the accuracy of ICS attack detection in Federated Learning under imbalanced sample conditions. |
| format | Article |
| id | doaj-art-0d933f94cdd94268bb231be09c90fe6d |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0d933f94cdd94268bb231be09c90fe6d2025-08-20T02:59:07ZengMDPI AGApplied Sciences2076-34172025-02-01155241610.3390/app15052416CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data GenerationShu Feng0Luhan Gao1Leyi Shi2Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaQingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, ChinaThe implementation of comprehensive security measures is a critical factor in the rapid growth of industrial control networks. Federated Learning has emerged as a viable solution for safeguarding privacy in machine learning. The effectiveness of pattern detection in models is diminished as a result of the difficulty in extracting attack information from extremely large datasets and obtaining an adequate number of examples for specific types of attacks. A robust Federated Learning method, CGFL, is introduced in this study to resolve the challenges presented by data distribution discrepancies and client class imbalance. By employing a data generation strategy to generate balanced datasets for each client, CGFL enhances the global model. It employs a data generator that integrates artificially generated data with the existing data from local clients by employing label correction and data generation techniques. The geometric median aggregation technique was implemented to enhance the security of the aggregation process. The model was simulated and evaluated using the CIC-IDS2017 dataset, NSL-KDD dataset, and CSE-CIC-IDS2018 dataset. The experimental results indicate that CGFL does an effective job of enhancing the accuracy of ICS attack detection in Federated Learning under imbalanced sample conditions.https://www.mdpi.com/2076-3417/15/5/2416intrusion detection systemFederated Learningdata augmentationgenerative adversarial networks |
| spellingShingle | Shu Feng Luhan Gao Leyi Shi CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation Applied Sciences intrusion detection system Federated Learning data augmentation generative adversarial networks |
| title | CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation |
| title_full | CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation |
| title_fullStr | CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation |
| title_full_unstemmed | CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation |
| title_short | CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation |
| title_sort | cgfl a robust federated learning approach for intrusion detection systems based on data generation |
| topic | intrusion detection system Federated Learning data augmentation generative adversarial networks |
| url | https://www.mdpi.com/2076-3417/15/5/2416 |
| work_keys_str_mv | AT shufeng cgflarobustfederatedlearningapproachforintrusiondetectionsystemsbasedondatageneration AT luhangao cgflarobustfederatedlearningapproachforintrusiondetectionsystemsbasedondatageneration AT leyishi cgflarobustfederatedlearningapproachforintrusiondetectionsystemsbasedondatageneration |