The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware
Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. This method is critical in the development of identifying certain objects, and provides the computational intelligence required to identify multiple objects a...
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Politeknik Negeri Padang
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43584/1/The%20Rise%20of%20Deep%20Learning%20in%20Cyber%20Security%20Bibliometric%20Analysis.pdf http://umpir.ump.edu.my/id/eprint/43584/ http://dx.doi.org/10.62527/joiv.8.3.1535 |
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| author | Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Mohd Zamri, Osman Alanda, Alde Erianda, Aldo Shahreen, Kasim Mohd Faizal, Ab Razak |
| author_facet | Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Mohd Zamri, Osman Alanda, Alde Erianda, Aldo Shahreen, Kasim Mohd Faizal, Ab Razak |
| author_sort | Nur Khairani, Kamarudin |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. This method is critical in the development of identifying certain objects, and provides the computational intelligence required to identify multiple objects and distinguish it between object A or Object B. On the other hand, malware is defined as malicious software that seeks to harm or disrupt computers and systems. Its main categories include viruses, worms, Trojan horses, spyware, adware, and ransomware. Hence, many deep learning researchers apply deep learning in their malware studies. However, few articles still investigate deep learning and malware in a bibliometric approach (productivity, research area, institutions, authors, impact journals, and keyword analysis). Hence, this paper reports bibliometric analysis used to discover current and future trends and gain new insights into the relationship between deep learning and malware. This paper’s discoveries include: Deployment of deep learning to detect domain generation algorithm (DGA) attacks; Deployment of deep learning to detect malware in Internet of Things (IoT); The rise of adversarial learning and adversarial attack using deep learning; The emergence of Android malware in deep learning; The deployment of transfer learning in malware research; and active authors on deep learning and malware research, including Soman KP, Vinayakumar R, and Zhang Y. |
| format | Article |
| id | my.ump.umpir.43584 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2024 |
| publisher | Politeknik Negeri Padang |
| record_format | eprints |
| spelling | my.ump.umpir.435842025-01-15T08:16:23Z http://umpir.ump.edu.my/id/eprint/43584/ The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Mohd Zamri, Osman Alanda, Alde Erianda, Aldo Shahreen, Kasim Mohd Faizal, Ab Razak QA75 Electronic computers. Computer science Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. This method is critical in the development of identifying certain objects, and provides the computational intelligence required to identify multiple objects and distinguish it between object A or Object B. On the other hand, malware is defined as malicious software that seeks to harm or disrupt computers and systems. Its main categories include viruses, worms, Trojan horses, spyware, adware, and ransomware. Hence, many deep learning researchers apply deep learning in their malware studies. However, few articles still investigate deep learning and malware in a bibliometric approach (productivity, research area, institutions, authors, impact journals, and keyword analysis). Hence, this paper reports bibliometric analysis used to discover current and future trends and gain new insights into the relationship between deep learning and malware. This paper’s discoveries include: Deployment of deep learning to detect domain generation algorithm (DGA) attacks; Deployment of deep learning to detect malware in Internet of Things (IoT); The rise of adversarial learning and adversarial attack using deep learning; The emergence of Android malware in deep learning; The deployment of transfer learning in malware research; and active authors on deep learning and malware research, including Soman KP, Vinayakumar R, and Zhang Y. Politeknik Negeri Padang 2024 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/43584/1/The%20Rise%20of%20Deep%20Learning%20in%20Cyber%20Security%20Bibliometric%20Analysis.pdf Nur Khairani, Kamarudin and Ahmad Firdaus, Zainal Abidin and Mohd Zamri, Osman and Alanda, Alde and Erianda, Aldo and Shahreen, Kasim and Mohd Faizal, Ab Razak (2024) The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware. International Journal on Informatics Visualization, 8 (3). 1398 -1435. ISSN 2549-9904. (Published) http://dx.doi.org/10.62527/joiv.8.3.1535 http://dx.doi.org/10.62527/joiv.8.3.1535 |
| spellingShingle | QA75 Electronic computers. Computer science Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Mohd Zamri, Osman Alanda, Alde Erianda, Aldo Shahreen, Kasim Mohd Faizal, Ab Razak The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware |
| title | The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware |
| title_full | The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware |
| title_fullStr | The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware |
| title_full_unstemmed | The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware |
| title_short | The rise of deep learning in cyber security: Bibliometric analysis of deep learning and malware |
| title_sort | rise of deep learning in cyber security: bibliometric analysis of deep learning and malware |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/43584/1/The%20Rise%20of%20Deep%20Learning%20in%20Cyber%20Security%20Bibliometric%20Analysis.pdf http://umpir.ump.edu.my/id/eprint/43584/ http://dx.doi.org/10.62527/joiv.8.3.1535 http://dx.doi.org/10.62527/joiv.8.3.1535 |
| url_provider | http://umpir.ump.edu.my/ |