Ransomware detection using the dynamic analysis and machine learning: A survey and research directions
Ransomware is an ill-famed malware that has received recognition because of its lethal and irrevocable effects on its victims. The irreparable loss caused due to ransomware requires the timely detection of these attacks. Several studies including surveys and reviews are conducted on the evolution, t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/100969/1/BanderAliSaleh2022_RansomwareDetectionUsingtheDynamicAnalysis.pdf |
| _version_ | 1796866975561940992 |
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| author | Urooj, Umara Al-rimy, Bander Ali Saleh Zainal, Anazida A. Ghaleb, Fuad A. Rassam, Murad |
| author_facet | Urooj, Umara Al-rimy, Bander Ali Saleh Zainal, Anazida A. Ghaleb, Fuad A. Rassam, Murad |
| author_sort | Urooj, Umara |
| collection | ePrints |
| description | Ransomware is an ill-famed malware that has received recognition because of its lethal and irrevocable effects on its victims. The irreparable loss caused due to ransomware requires the timely detection of these attacks. Several studies including surveys and reviews are conducted on the evolution, taxonomy, trends, threats, and countermeasures of ransomware. Some of these studies were specifically dedicated to IoT and android platforms. However, there is not a single study in the available literature that addresses the significance of dynamic analysis for the ransomware detection studies for all the targeted platforms. This study also provides the information about the datasets collection from its sources, which were utilized in the ransomware detection studies of the diverse platforms. This study is also distinct in terms of providing a survey about the ransomware detection studies utilizing machine learning, deep learning, and blend of both techniques while capitalizing on the advantages of dynamic analysis for the ransomware detection. The presented work considers the ransomware detection studies conducted from 2019 to 2021. This study provides an ample list of future directions which will pave the way for future research. |
| first_indexed | 2024-03-05T21:20:14Z |
| format | Article |
| id | utm.eprints-100969 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:20:14Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-1009692023-05-18T06:04:53Z http://eprints.utm.my/100969/ Ransomware detection using the dynamic analysis and machine learning: A survey and research directions Urooj, Umara Al-rimy, Bander Ali Saleh Zainal, Anazida A. Ghaleb, Fuad A. Rassam, Murad QA75 Electronic computers. Computer science Ransomware is an ill-famed malware that has received recognition because of its lethal and irrevocable effects on its victims. The irreparable loss caused due to ransomware requires the timely detection of these attacks. Several studies including surveys and reviews are conducted on the evolution, taxonomy, trends, threats, and countermeasures of ransomware. Some of these studies were specifically dedicated to IoT and android platforms. However, there is not a single study in the available literature that addresses the significance of dynamic analysis for the ransomware detection studies for all the targeted platforms. This study also provides the information about the datasets collection from its sources, which were utilized in the ransomware detection studies of the diverse platforms. This study is also distinct in terms of providing a survey about the ransomware detection studies utilizing machine learning, deep learning, and blend of both techniques while capitalizing on the advantages of dynamic analysis for the ransomware detection. The presented work considers the ransomware detection studies conducted from 2019 to 2021. This study provides an ample list of future directions which will pave the way for future research. MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/100969/1/BanderAliSaleh2022_RansomwareDetectionUsingtheDynamicAnalysis.pdf Urooj, Umara and Al-rimy, Bander Ali Saleh and Zainal, Anazida and A. Ghaleb, Fuad and A. Rassam, Murad (2022) Ransomware detection using the dynamic analysis and machine learning: A survey and research directions. Applied Sciences (Switzerland), 12 (1). pp. 1-45. ISSN 2076-3417 http://dx.doi.org/10.3390/app12010172 DOI : 10.3390/app12010172 |
| spellingShingle | QA75 Electronic computers. Computer science Urooj, Umara Al-rimy, Bander Ali Saleh Zainal, Anazida A. Ghaleb, Fuad A. Rassam, Murad Ransomware detection using the dynamic analysis and machine learning: A survey and research directions |
| title | Ransomware detection using the dynamic analysis and machine learning: A survey and research directions |
| title_full | Ransomware detection using the dynamic analysis and machine learning: A survey and research directions |
| title_fullStr | Ransomware detection using the dynamic analysis and machine learning: A survey and research directions |
| title_full_unstemmed | Ransomware detection using the dynamic analysis and machine learning: A survey and research directions |
| title_short | Ransomware detection using the dynamic analysis and machine learning: A survey and research directions |
| title_sort | ransomware detection using the dynamic analysis and machine learning a survey and research directions |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/100969/1/BanderAliSaleh2022_RansomwareDetectionUsingtheDynamicAnalysis.pdf |
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