Deep learning-based classification model for botnet attack detection
otnets are vectors through which hackers can seize control of multiple systems and conduct malicious activities. Researchers have proposed multiple solutions to detect and identify botnets in real time. However, these proposed solutions have difficulties in keeping pace with the rapid evolution of b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerLink
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/28296/ http://umpir.ump.edu.my/id/eprint/28296/1/Deep%20learning-based%20classification%20model1.pdf |
| _version_ | 1848823016871428096 |
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| author | Ahmed, Abdulghani Ali Jabbar, Waheb A. Sadiq, Ali Safa Patel, Hiran |
| author_facet | Ahmed, Abdulghani Ali Jabbar, Waheb A. Sadiq, Ali Safa Patel, Hiran |
| author_sort | Ahmed, Abdulghani Ali |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | otnets are vectors through which hackers can seize control of multiple systems and conduct malicious activities. Researchers have proposed multiple solutions to detect and identify botnets in real time. However, these proposed solutions have difficulties in keeping pace with the rapid evolution of botnets. This paper proposes a model for detecting botnets using deep learning to identify zero-day botnet attacks in real time. The proposed model is trained and evaluated on a CTU-13 dataset with multiple neural network designs and hidden layers. Results demonstrate that the deep-learning artificial neural network model can accurately and efficiently identify botnets. |
| first_indexed | 2025-11-15T02:50:26Z |
| format | Article |
| id | ump-28296 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:50:26Z |
| publishDate | 2020 |
| publisher | SpringerLink |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-282962020-05-06T06:53:10Z http://umpir.ump.edu.my/id/eprint/28296/ Deep learning-based classification model for botnet attack detection Ahmed, Abdulghani Ali Jabbar, Waheb A. Sadiq, Ali Safa Patel, Hiran TK Electrical engineering. Electronics Nuclear engineering otnets are vectors through which hackers can seize control of multiple systems and conduct malicious activities. Researchers have proposed multiple solutions to detect and identify botnets in real time. However, these proposed solutions have difficulties in keeping pace with the rapid evolution of botnets. This paper proposes a model for detecting botnets using deep learning to identify zero-day botnet attacks in real time. The proposed model is trained and evaluated on a CTU-13 dataset with multiple neural network designs and hidden layers. Results demonstrate that the deep-learning artificial neural network model can accurately and efficiently identify botnets. SpringerLink 2020 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28296/1/Deep%20learning-based%20classification%20model1.pdf Ahmed, Abdulghani Ali and Jabbar, Waheb A. and Sadiq, Ali Safa and Patel, Hiran (2020) Deep learning-based classification model for botnet attack detection. Journal of Ambient Intelligence and Humanized Computing. pp. 1-10. ISSN 1868-5145. (Published) https://doi.org/10.1007/s12652-020-01848-9 https://doi.org/10.1007/s12652-020-01848-9 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ahmed, Abdulghani Ali Jabbar, Waheb A. Sadiq, Ali Safa Patel, Hiran Deep learning-based classification model for botnet attack detection |
| title | Deep learning-based classification model for botnet attack detection |
| title_full | Deep learning-based classification model for botnet attack detection |
| title_fullStr | Deep learning-based classification model for botnet attack detection |
| title_full_unstemmed | Deep learning-based classification model for botnet attack detection |
| title_short | Deep learning-based classification model for botnet attack detection |
| title_sort | deep learning-based classification model for botnet attack detection |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/28296/ http://umpir.ump.edu.my/id/eprint/28296/ http://umpir.ump.edu.my/id/eprint/28296/ http://umpir.ump.edu.my/id/eprint/28296/1/Deep%20learning-based%20classification%20model1.pdf |