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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/1/172 |
| _version_ | 1797499674065633280 |
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| author | Umara Urooj Bander Ali Saleh Al-rimy Anazida Zainal Fuad A. Ghaleb Murad A. Rassam |
| author_facet | Umara Urooj Bander Ali Saleh Al-rimy Anazida Zainal Fuad A. Ghaleb Murad A. Rassam |
| author_sort | Umara Urooj |
| collection | DOAJ |
| 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 | 2024-03-10T03:50:48Z |
| format | Article |
| id | doaj.art-f72437eac90440179d40dccbb325997f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:50:48Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f72437eac90440179d40dccbb325997f2023-11-23T11:08:51ZengMDPI AGApplied Sciences2076-34172021-12-0112117210.3390/app12010172Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research DirectionsUmara Urooj0Bander Ali Saleh Al-rimy1Anazida Zainal2Fuad A. Ghaleb3Murad A. Rassam4School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81300, Johor, MalaysiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81300, Johor, MalaysiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81300, Johor, MalaysiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81300, Johor, MalaysiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaRansomware 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.https://www.mdpi.com/2076-3417/12/1/172machine learningdeep learningransomwareransomware analysisdynamic analysisransomware detection |
| spellingShingle | Umara Urooj Bander Ali Saleh Al-rimy Anazida Zainal Fuad A. Ghaleb Murad A. Rassam Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions Applied Sciences machine learning deep learning ransomware ransomware analysis dynamic analysis ransomware detection |
| 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 | machine learning deep learning ransomware ransomware analysis dynamic analysis ransomware detection |
| url | https://www.mdpi.com/2076-3417/12/1/172 |
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