Lightweight malicious domain name detection model based on separable convolution
The application of artificial intelligence in the detection of malicious domain names needs to consider both accuracy and calculation speed,which can make it closer to the actual application.Based on the above considerations,a lightweight malicious domain name detection model based on separable conv...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2020-12-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020084 |
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| _version_ | 1841529913201393664 |
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| author | Luhui YANG Huiwen BAI Guangjie LIU Yuewei DAI |
| author_facet | Luhui YANG Huiwen BAI Guangjie LIU Yuewei DAI |
| author_sort | Luhui YANG |
| collection | DOAJ |
| description | The application of artificial intelligence in the detection of malicious domain names needs to consider both accuracy and calculation speed,which can make it closer to the actual application.Based on the above considerations,a lightweight malicious domain name detection model based on separable convolution was proposed.The model uses a separable convolution structure.It first applies depthwise convolution on every input channel,and then performs pointwise convolution on all output channels.This can effectively reduce the parameters of convolution process without impacting the effectiveness of convolution feature extraction,and realize faster convolution process while keeping high accuracy.To improve the detection accuracy considering the imbalance of the number and difficulty of positive and negative samples,a focal loss function was introduced in the training process of the model.The proposed algorithm was compared with three typical deep-learning-based detection models on a public data set.Experimental results denote that the proposed algorithm achieves detection accuracy close to the state-of-the-art model,and can significantly improve model inference speed on CPU. |
| format | Article |
| id | doaj-art-bec25a0fed594a8a846533aeff1b35d3 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2020-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-bec25a0fed594a8a846533aeff1b35d32025-01-15T03:14:35ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2020-12-01611212059562102Lightweight malicious domain name detection model based on separable convolutionLuhui YANGHuiwen BAIGuangjie LIUYuewei DAIThe application of artificial intelligence in the detection of malicious domain names needs to consider both accuracy and calculation speed,which can make it closer to the actual application.Based on the above considerations,a lightweight malicious domain name detection model based on separable convolution was proposed.The model uses a separable convolution structure.It first applies depthwise convolution on every input channel,and then performs pointwise convolution on all output channels.This can effectively reduce the parameters of convolution process without impacting the effectiveness of convolution feature extraction,and realize faster convolution process while keeping high accuracy.To improve the detection accuracy considering the imbalance of the number and difficulty of positive and negative samples,a focal loss function was introduced in the training process of the model.The proposed algorithm was compared with three typical deep-learning-based detection models on a public data set.Experimental results denote that the proposed algorithm achieves detection accuracy close to the state-of-the-art model,and can significantly improve model inference speed on CPU.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020084separable convolutiondomain generation algorithmdeep learningcyber security |
| spellingShingle | Luhui YANG Huiwen BAI Guangjie LIU Yuewei DAI Lightweight malicious domain name detection model based on separable convolution 网络与信息安全学报 separable convolution domain generation algorithm deep learning cyber security |
| title | Lightweight malicious domain name detection model based on separable convolution |
| title_full | Lightweight malicious domain name detection model based on separable convolution |
| title_fullStr | Lightweight malicious domain name detection model based on separable convolution |
| title_full_unstemmed | Lightweight malicious domain name detection model based on separable convolution |
| title_short | Lightweight malicious domain name detection model based on separable convolution |
| title_sort | lightweight malicious domain name detection model based on separable convolution |
| topic | separable convolution domain generation algorithm deep learning cyber security |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020084 |
| work_keys_str_mv | AT luhuiyang lightweightmaliciousdomainnamedetectionmodelbasedonseparableconvolution AT huiwenbai lightweightmaliciousdomainnamedetectionmodelbasedonseparableconvolution AT guangjieliu lightweightmaliciousdomainnamedetectionmodelbasedonseparableconvolution AT yueweidai lightweightmaliciousdomainnamedetectionmodelbasedonseparableconvolution |