An intrusion detection method based on depthwise separable convolution and attention mechanism
In order to improve the accuracy of multi-classification in network intrusion detection, an intrusion detection method was proposed based on depthwise separable convolution and attention mechanism.By constructing a cascade structure combining depthwise separable convolution and long-term and short-t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2023-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2023.00307 |
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| _version_ | 1850212419088941056 |
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| author | Zhifei ZHANG Feng LIU Yiyang GE Shuo LI Yu ZHANG Ke XIONG |
| author_facet | Zhifei ZHANG Feng LIU Yiyang GE Shuo LI Yu ZHANG Ke XIONG |
| author_sort | Zhifei ZHANG |
| collection | DOAJ |
| description | In order to improve the accuracy of multi-classification in network intrusion detection, an intrusion detection method was proposed based on depthwise separable convolution and attention mechanism.By constructing a cascade structure combining depthwise separable convolution and long-term and short-term memory networks, the spatial and temporal features of network traffic data can be better extracted.A mixed-domain attention mechanism was introduced to enhance the detection performance.To solve the problem of low detection rate in some samples, a data balance strategy based on the combination of the variational auto-encoder (VAE) the generative adversarial network (GAN) and was designed, which can effectively cope with imbalanced datasets and improve the adaptability of the proposed detection method.The experimental results show that the proposed method is able to achieve 99.80%, 99.32%, and 83.87% accuracy on the CICIDS-2017, NSL-KDD and UNSW-NB15 datasets, which is improved by 0.6%, 0.5%, and 2.3%, respectively. |
| format | Article |
| id | doaj-art-c62c80ccc7134d7b974e29e92bc0cebe |
| institution | OA Journals |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2023-03-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-c62c80ccc7134d7b974e29e92bc0cebe2025-08-20T02:09:21ZzhoChina InfoCom Media Group物联网学报2096-37502023-03-017495959579206An intrusion detection method based on depthwise separable convolution and attention mechanismZhifei ZHANGFeng LIUYiyang GEShuo LIYu ZHANGKe XIONGIn order to improve the accuracy of multi-classification in network intrusion detection, an intrusion detection method was proposed based on depthwise separable convolution and attention mechanism.By constructing a cascade structure combining depthwise separable convolution and long-term and short-term memory networks, the spatial and temporal features of network traffic data can be better extracted.A mixed-domain attention mechanism was introduced to enhance the detection performance.To solve the problem of low detection rate in some samples, a data balance strategy based on the combination of the variational auto-encoder (VAE) the generative adversarial network (GAN) and was designed, which can effectively cope with imbalanced datasets and improve the adaptability of the proposed detection method.The experimental results show that the proposed method is able to achieve 99.80%, 99.32%, and 83.87% accuracy on the CICIDS-2017, NSL-KDD and UNSW-NB15 datasets, which is improved by 0.6%, 0.5%, and 2.3%, respectively.http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2023.00307deep learning;intrusion detection;attention mechanism;generative adversarial network |
| spellingShingle | Zhifei ZHANG Feng LIU Yiyang GE Shuo LI Yu ZHANG Ke XIONG An intrusion detection method based on depthwise separable convolution and attention mechanism 物联网学报 deep learning;intrusion detection;attention mechanism;generative adversarial network |
| title | An intrusion detection method based on depthwise separable convolution and attention mechanism |
| title_full | An intrusion detection method based on depthwise separable convolution and attention mechanism |
| title_fullStr | An intrusion detection method based on depthwise separable convolution and attention mechanism |
| title_full_unstemmed | An intrusion detection method based on depthwise separable convolution and attention mechanism |
| title_short | An intrusion detection method based on depthwise separable convolution and attention mechanism |
| title_sort | intrusion detection method based on depthwise separable convolution and attention mechanism |
| topic | deep learning;intrusion detection;attention mechanism;generative adversarial network |
| url | http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2023.00307 |
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