Malicious DNS traffic detection based neural networks
To solve the problems of low detection accuracy and speed caused by low efficiency in extracting traffic features using machine learning to detect malicious DNS traffic, a malicious DNS traffic detection method FDS-DL was proposed, which combines frequency domain feature aggregation analysis and neu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024232/ |
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| _version_ | 1841537100762054656 |
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| author | SHAN Kangkang YUAN Shuhong CHEN Wenzhi WANG Zhibo |
| author_facet | SHAN Kangkang YUAN Shuhong CHEN Wenzhi WANG Zhibo |
| author_sort | SHAN Kangkang |
| collection | DOAJ |
| description | To solve the problems of low detection accuracy and speed caused by low efficiency in extracting traffic features using machine learning to detect malicious DNS traffic, a malicious DNS traffic detection method FDS-DL was proposed, which combines frequency domain feature aggregation analysis and neural networks algorithms. Firstly, DNS traffic was converted from time-domain space to frequency-domain space through discrete Fourier transform, which could significantly compress the data scale while retaining key log information. Then, convolutional neural network was used to classify the processed frequency domain sequence data. The experimental results show that compared with several mainstream detection methods, FDS-DL has a higher accuracy in identifying malicious DNS traffic and F1_score is optimal. |
| format | Article |
| id | doaj-art-8dcc165947994d9c8810cc85b9e2cccc |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8dcc165947994d9c8810cc85b9e2cccc2025-01-14T08:46:36ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-11-01451679661496Malicious DNS traffic detection based neural networksSHAN KangkangYUAN ShuhongCHEN WenzhiWANG ZhiboTo solve the problems of low detection accuracy and speed caused by low efficiency in extracting traffic features using machine learning to detect malicious DNS traffic, a malicious DNS traffic detection method FDS-DL was proposed, which combines frequency domain feature aggregation analysis and neural networks algorithms. Firstly, DNS traffic was converted from time-domain space to frequency-domain space through discrete Fourier transform, which could significantly compress the data scale while retaining key log information. Then, convolutional neural network was used to classify the processed frequency domain sequence data. The experimental results show that compared with several mainstream detection methods, FDS-DL has a higher accuracy in identifying malicious DNS traffic and F1_score is optimal.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024232/frequency domainDFTneural networkconvolutional neural networkmalicious domain name |
| spellingShingle | SHAN Kangkang YUAN Shuhong CHEN Wenzhi WANG Zhibo Malicious DNS traffic detection based neural networks Tongxin xuebao frequency domain DFT neural network convolutional neural network malicious domain name |
| title | Malicious DNS traffic detection based neural networks |
| title_full | Malicious DNS traffic detection based neural networks |
| title_fullStr | Malicious DNS traffic detection based neural networks |
| title_full_unstemmed | Malicious DNS traffic detection based neural networks |
| title_short | Malicious DNS traffic detection based neural networks |
| title_sort | malicious dns traffic detection based neural networks |
| topic | frequency domain DFT neural network convolutional neural network malicious domain name |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024232/ |
| work_keys_str_mv | AT shankangkang maliciousdnstrafficdetectionbasedneuralnetworks AT yuanshuhong maliciousdnstrafficdetectionbasedneuralnetworks AT chenwenzhi maliciousdnstrafficdetectionbasedneuralnetworks AT wangzhibo maliciousdnstrafficdetectionbasedneuralnetworks |