A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning
With the rapid development of network technology, a variety of new malicious attacks appear while attack methods are constantly updated. As the attackers exploit the vulnerabilities of popular third-party components to invade target websites, further improving the classification accuracy of maliciou...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.2024509 |
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| _version_ | 1849766532022796288 |
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| author | Shaokang Cai Dezhi Han Xinming Yin Dun Li Chin-Chen Chang |
| author_facet | Shaokang Cai Dezhi Han Xinming Yin Dun Li Chin-Chen Chang |
| author_sort | Shaokang Cai |
| collection | DOAJ |
| description | With the rapid development of network technology, a variety of new malicious attacks appear while attack methods are constantly updated. As the attackers exploit the vulnerabilities of popular third-party components to invade target websites, further improving the classification accuracy of malicious network traffic is the key to improving the performance of abnormal traffic detection. Existing intrusion detection systems may suffer from incomplete feature extraction and low classification accuracy. Thus, this paper proposes an efficient hybrid parallel deep learning model (HPM) for intrusion detection based on margin learning. First, HPM constructs two parallel CNN architectures and fuses the spatial features obtained through full convolution. Secondly, the temporal information of the fused features is parsed separately using two parallel LSTMs. Finally, the extracted spatial-temporal features are fed into the CosMargin classifier for classification detection after global convolution and global pooling. Besides, this paper proposes an improved traffic feature extraction method, which not only reduces redundant features but also speeds up the convergence speed of the network. In the experiment, our HPM has achieved 99% detection accuracy of each malicious class, ranging from 5%–10% improvement with other models, which demonstrates the superiority of our proposed model. |
| format | Article |
| id | doaj-art-ad24de4a5d7b47f4b978757b5f2cb8c5 |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-ad24de4a5d7b47f4b978757b5f2cb8c52025-08-20T03:04:31ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134155157710.1080/09540091.2021.20245092024509A Hybrid parallel deep learning model for efficient intrusion detection based on metric learningShaokang Cai0Dezhi Han1Xinming Yin2Dun Li3Chin-Chen Chang4Shanghai Maritime UniversityShanghai Maritime UniversityEast China University of Science and TechnologyShanghai Maritime UniversityFeng Chia UniversityWith the rapid development of network technology, a variety of new malicious attacks appear while attack methods are constantly updated. As the attackers exploit the vulnerabilities of popular third-party components to invade target websites, further improving the classification accuracy of malicious network traffic is the key to improving the performance of abnormal traffic detection. Existing intrusion detection systems may suffer from incomplete feature extraction and low classification accuracy. Thus, this paper proposes an efficient hybrid parallel deep learning model (HPM) for intrusion detection based on margin learning. First, HPM constructs two parallel CNN architectures and fuses the spatial features obtained through full convolution. Secondly, the temporal information of the fused features is parsed separately using two parallel LSTMs. Finally, the extracted spatial-temporal features are fed into the CosMargin classifier for classification detection after global convolution and global pooling. Besides, this paper proposes an improved traffic feature extraction method, which not only reduces redundant features but also speeds up the convergence speed of the network. In the experiment, our HPM has achieved 99% detection accuracy of each malicious class, ranging from 5%–10% improvement with other models, which demonstrates the superiority of our proposed model.http://dx.doi.org/10.1080/09540091.2021.2024509network intrusion detectiondeep learninghybrid parallel networkmulti-classificationfeature fusionmetric learning |
| spellingShingle | Shaokang Cai Dezhi Han Xinming Yin Dun Li Chin-Chen Chang A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning Connection Science network intrusion detection deep learning hybrid parallel network multi-classification feature fusion metric learning |
| title | A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning |
| title_full | A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning |
| title_fullStr | A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning |
| title_full_unstemmed | A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning |
| title_short | A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning |
| title_sort | hybrid parallel deep learning model for efficient intrusion detection based on metric learning |
| topic | network intrusion detection deep learning hybrid parallel network multi-classification feature fusion metric learning |
| url | http://dx.doi.org/10.1080/09540091.2021.2024509 |
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