CPDGA: foresee future DGA using proactive conformal propagation
Attackers dynamically register domain names through the domain generation algorithm (DGA) to support malware activities. The continuous evolution of malicious domain names leads to the phenomenon of concept drift, rendering the existing detection techniques based on continual learning models less ef...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2025-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025106/ |
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| _version_ | 1849709219856515072 |
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| author | LIU Shuangshuang WANG Zhi DONG Yimeng LI Wanpeng |
| author_facet | LIU Shuangshuang WANG Zhi DONG Yimeng LI Wanpeng |
| author_sort | LIU Shuangshuang |
| collection | DOAJ |
| description | Attackers dynamically register domain names through the domain generation algorithm (DGA) to support malware activities. The continuous evolution of malicious domain names leads to the phenomenon of concept drift, rendering the existing detection techniques based on continual learning models less effective over time. To address this threat, by combining conformal prediction and conformal clustering, a foresee future DGA using proactive conformal propagation (CPDGA) was proposed. Experiments were conducted using datasets of malicious and benign domain names from 2019 to 2023. CPDGA was applied to mitigate the effect of concept drift. As a result, the impact of concept drift was effectively reduced. The detection accuracy was improved by 20.4%. Additionally, CPDGA achieves an accuracy rate of 96.42% in detecting the domain names generated by 13 latest adversarial models, showing its strong robustness and adaptability. |
| format | Article |
| id | doaj-art-e8fceb82d6494ed4b319172a799c2fc2 |
| institution | DOAJ |
| issn | 1000-436X |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-e8fceb82d6494ed4b319172a799c2fc22025-08-20T03:15:22ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2025-06-01461831114257040CPDGA: foresee future DGA using proactive conformal propagationLIU ShuangshuangWANG ZhiDONG YimengLI WanpengAttackers dynamically register domain names through the domain generation algorithm (DGA) to support malware activities. The continuous evolution of malicious domain names leads to the phenomenon of concept drift, rendering the existing detection techniques based on continual learning models less effective over time. To address this threat, by combining conformal prediction and conformal clustering, a foresee future DGA using proactive conformal propagation (CPDGA) was proposed. Experiments were conducted using datasets of malicious and benign domain names from 2019 to 2023. CPDGA was applied to mitigate the effect of concept drift. As a result, the impact of concept drift was effectively reduced. The detection accuracy was improved by 20.4%. Additionally, CPDGA achieves an accuracy rate of 96.42% in detecting the domain names generated by 13 latest adversarial models, showing its strong robustness and adaptability.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025106/domain generation algorithmconcept driftconformal predictionconformal clusteringadversarial model |
| spellingShingle | LIU Shuangshuang WANG Zhi DONG Yimeng LI Wanpeng CPDGA: foresee future DGA using proactive conformal propagation Tongxin xuebao domain generation algorithm concept drift conformal prediction conformal clustering adversarial model |
| title | CPDGA: foresee future DGA using proactive conformal propagation |
| title_full | CPDGA: foresee future DGA using proactive conformal propagation |
| title_fullStr | CPDGA: foresee future DGA using proactive conformal propagation |
| title_full_unstemmed | CPDGA: foresee future DGA using proactive conformal propagation |
| title_short | CPDGA: foresee future DGA using proactive conformal propagation |
| title_sort | cpdga foresee future dga using proactive conformal propagation |
| topic | domain generation algorithm concept drift conformal prediction conformal clustering adversarial model |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2025106/ |
| work_keys_str_mv | AT liushuangshuang cpdgaforeseefuturedgausingproactiveconformalpropagation AT wangzhi cpdgaforeseefuturedgausingproactiveconformalpropagation AT dongyimeng cpdgaforeseefuturedgausingproactiveconformalpropagation AT liwanpeng cpdgaforeseefuturedgausingproactiveconformalpropagation |