Intrusion detection model of random attention capsule network based on variable fusion
In order to enhance the accuracy and generalization of the detection model,an intrusion detection model of random attention capsule network with variable fusion was proposed.Through dynamic feature fusion,the model could better capture data features.At the same time,random attention mechanism was us...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020220/ |
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| _version_ | 1841539291706032128 |
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| author | Xinglan ZHANG Shenglin YIN |
| author_facet | Xinglan ZHANG Shenglin YIN |
| author_sort | Xinglan ZHANG |
| collection | DOAJ |
| description | In order to enhance the accuracy and generalization of the detection model,an intrusion detection model of random attention capsule network with variable fusion was proposed.Through dynamic feature fusion,the model could better capture data features.At the same time,random attention mechanism was used to reduce the dependence on training data and make the model more generalization.The model was validated on NSL-KDD and UNSW-NB15 datasets.The experimental results show that the accuracy of the model on the two test sets is 99.49% and 98.60% respectively. |
| format | Article |
| id | doaj-art-948e06a4751545278c4432543cb93de2 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-948e06a4751545278c4432543cb93de22025-01-14T07:21:11ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-11-014116016859738766Intrusion detection model of random attention capsule network based on variable fusionXinglan ZHANGShenglin YINIn order to enhance the accuracy and generalization of the detection model,an intrusion detection model of random attention capsule network with variable fusion was proposed.Through dynamic feature fusion,the model could better capture data features.At the same time,random attention mechanism was used to reduce the dependence on training data and make the model more generalization.The model was validated on NSL-KDD and UNSW-NB15 datasets.The experimental results show that the accuracy of the model on the two test sets is 99.49% and 98.60% respectively.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020220/deep learningintrusion detectioncyberspace securitycapsule networkrandom attention |
| spellingShingle | Xinglan ZHANG Shenglin YIN Intrusion detection model of random attention capsule network based on variable fusion Tongxin xuebao deep learning intrusion detection cyberspace security capsule network random attention |
| title | Intrusion detection model of random attention capsule network based on variable fusion |
| title_full | Intrusion detection model of random attention capsule network based on variable fusion |
| title_fullStr | Intrusion detection model of random attention capsule network based on variable fusion |
| title_full_unstemmed | Intrusion detection model of random attention capsule network based on variable fusion |
| title_short | Intrusion detection model of random attention capsule network based on variable fusion |
| title_sort | intrusion detection model of random attention capsule network based on variable fusion |
| topic | deep learning intrusion detection cyberspace security capsule network random attention |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020220/ |
| work_keys_str_mv | AT xinglanzhang intrusiondetectionmodelofrandomattentioncapsulenetworkbasedonvariablefusion AT shenglinyin intrusiondetectionmodelofrandomattentioncapsulenetworkbasedonvariablefusion |