Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection
With the rapid advancement of network technologies, cyberthreats have become increasingly sophisticated, posing significant challenges to traditional intrusion detection systems. Conventional machine learning and deep learning approaches frequently experience performance degradation when confronted...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4851 |
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| author | Sunghyuk Lee Donghwan Roh Jaehak Yu Daesung Moon Jonghyuk Lee Ji-Hoon Bae |
| author_facet | Sunghyuk Lee Donghwan Roh Jaehak Yu Daesung Moon Jonghyuk Lee Ji-Hoon Bae |
| author_sort | Sunghyuk Lee |
| collection | DOAJ |
| description | With the rapid advancement of network technologies, cyberthreats have become increasingly sophisticated, posing significant challenges to traditional intrusion detection systems. Conventional machine learning and deep learning approaches frequently experience performance degradation when confronted with imbalanced datasets and novel attack vectors. To address these limitations, this study proposes a deep learning-based intrusion detection framework that employs feature fusion through incremental transfer learning between source and target domains. The proposed architecture integrates convolutional neural networks (CNNs) with an attention mechanism to extract and aggregate salient features, thereby enhancing the model’s discriminative capacity between normal traffic and various network attack categories. Experimental results demonstrate that the proposed model achieves a detection accuracy of 94.21% even when trained on only 33% of the available data, outperforming conventional models. These findings underscore the effectiveness of the proposed feature fusion strategy via transfer learning in improving detection capabilities within dynamic and evolving cyberthreat environments. |
| format | Article |
| id | doaj-art-99ce88a9fce44a058f4b0810a09525df |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-99ce88a9fce44a058f4b0810a09525df2025-08-20T03:52:57ZengMDPI AGApplied Sciences2076-34172025-04-01159485110.3390/app15094851Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion DetectionSunghyuk Lee0Donghwan Roh1Jaehak Yu2Daesung Moon3Jonghyuk Lee4Ji-Hoon Bae5Department of AI and Big Data Engineering, Daegu Catholic University, Gyeongsan-si 38430, Republic of KoreaDepartment of AI and Big Data Engineering, Daegu Catholic University, Gyeongsan-si 38430, Republic of KoreaElectronics and Telecommunications Research Institute, Daejeon 34129, Republic of KoreaElectronics and Telecommunications Research Institute, Daejeon 34129, Republic of KoreaDepartment of AI and Big Data Engineering, Daegu Catholic University, Gyeongsan-si 38430, Republic of KoreaDepartment of Computer Education, Korea National University of Education, Cheongju-si 28173, Republic of KoreaWith the rapid advancement of network technologies, cyberthreats have become increasingly sophisticated, posing significant challenges to traditional intrusion detection systems. Conventional machine learning and deep learning approaches frequently experience performance degradation when confronted with imbalanced datasets and novel attack vectors. To address these limitations, this study proposes a deep learning-based intrusion detection framework that employs feature fusion through incremental transfer learning between source and target domains. The proposed architecture integrates convolutional neural networks (CNNs) with an attention mechanism to extract and aggregate salient features, thereby enhancing the model’s discriminative capacity between normal traffic and various network attack categories. Experimental results demonstrate that the proposed model achieves a detection accuracy of 94.21% even when trained on only 33% of the available data, outperforming conventional models. These findings underscore the effectiveness of the proposed feature fusion strategy via transfer learning in improving detection capabilities within dynamic and evolving cyberthreat environments.https://www.mdpi.com/2076-3417/15/9/4851network intrusion detectiontransfer learningdeep learningfeature fusion |
| spellingShingle | Sunghyuk Lee Donghwan Roh Jaehak Yu Daesung Moon Jonghyuk Lee Ji-Hoon Bae Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection Applied Sciences network intrusion detection transfer learning deep learning feature fusion |
| title | Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection |
| title_full | Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection |
| title_fullStr | Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection |
| title_full_unstemmed | Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection |
| title_short | Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection |
| title_sort | deep feature fusion via transfer learning for multi class network intrusion detection |
| topic | network intrusion detection transfer learning deep learning feature fusion |
| url | https://www.mdpi.com/2076-3417/15/9/4851 |
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