Detection of malicious domain name based on a classifier combination
As a fundamental service on the internet,domain name system (DNS) can inevitably be abused by malicious activities.Based on the studies of Botnets and other malwares which made use of the domain generation algorithm (DGA),and researches on current major techniques of malicious domain detection,a mal...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2020-05-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020150/ |
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| _version_ | 1841530695068942336 |
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| author | Jiantao SHENG Maofei CHEN Dongxin LIU Laifu WANG Guoshui SHI Huamin JIN |
| author_facet | Jiantao SHENG Maofei CHEN Dongxin LIU Laifu WANG Guoshui SHI Huamin JIN |
| author_sort | Jiantao SHENG |
| collection | DOAJ |
| description | As a fundamental service on the internet,domain name system (DNS) can inevitably be abused by malicious activities.Based on the studies of Botnets and other malwares which made use of the domain generation algorithm (DGA),and researches on current major techniques of malicious domain detection,a malicious domain detection framework based on a classifier combination was proposed.The framework applied the support vector machine (SVM) as its main classifier and combined the naive Bayes classifier (NBC) supportively with some statistical characteristics.Experiment result demonstrates that the framework outperformes current techniques in the offline-training time and the capability of detecting unknow malicious domain families,which satisfies the requirement of internet service provider (ISP) to detect and analyze malicious domainson the internet. |
| format | Article |
| id | doaj-art-b4c6ff0f801e4245a88ebd2eac9661c2 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2020-05-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-b4c6ff0f801e4245a88ebd2eac9661c22025-01-15T03:00:42ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012020-05-0136475559583063Detection of malicious domain name based on a classifier combinationJiantao SHENGMaofei CHENDongxin LIULaifu WANGGuoshui SHIHuamin JINAs a fundamental service on the internet,domain name system (DNS) can inevitably be abused by malicious activities.Based on the studies of Botnets and other malwares which made use of the domain generation algorithm (DGA),and researches on current major techniques of malicious domain detection,a malicious domain detection framework based on a classifier combination was proposed.The framework applied the support vector machine (SVM) as its main classifier and combined the naive Bayes classifier (NBC) supportively with some statistical characteristics.Experiment result demonstrates that the framework outperformes current techniques in the offline-training time and the capability of detecting unknow malicious domain families,which satisfies the requirement of internet service provider (ISP) to detect and analyze malicious domainson the internet.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020150/malicious domain nameBotnetmachine learningdeep learningclassifier combination |
| spellingShingle | Jiantao SHENG Maofei CHEN Dongxin LIU Laifu WANG Guoshui SHI Huamin JIN Detection of malicious domain name based on a classifier combination Dianxin kexue malicious domain name Botnet machine learning deep learning classifier combination |
| title | Detection of malicious domain name based on a classifier combination |
| title_full | Detection of malicious domain name based on a classifier combination |
| title_fullStr | Detection of malicious domain name based on a classifier combination |
| title_full_unstemmed | Detection of malicious domain name based on a classifier combination |
| title_short | Detection of malicious domain name based on a classifier combination |
| title_sort | detection of malicious domain name based on a classifier combination |
| topic | malicious domain name Botnet machine learning deep learning classifier combination |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020150/ |
| work_keys_str_mv | AT jiantaosheng detectionofmaliciousdomainnamebasedonaclassifiercombination AT maofeichen detectionofmaliciousdomainnamebasedonaclassifiercombination AT dongxinliu detectionofmaliciousdomainnamebasedonaclassifiercombination AT laifuwang detectionofmaliciousdomainnamebasedonaclassifiercombination AT guoshuishi detectionofmaliciousdomainnamebasedonaclassifiercombination AT huaminjin detectionofmaliciousdomainnamebasedonaclassifiercombination |