A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook
Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/5/1837 |
| _version_ | 1797473772104581120 |
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| author | Abdullah Alqahtani Frederick T. Sheldon |
| author_facet | Abdullah Alqahtani Frederick T. Sheldon |
| author_sort | Abdullah Alqahtani |
| collection | DOAJ |
| description | Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope. |
| first_indexed | 2024-03-09T20:21:09Z |
| format | Article |
| id | doaj.art-363bcd3b98d64eb8afd8f87eeda3d995 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:21:09Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-363bcd3b98d64eb8afd8f87eeda3d9952023-11-23T23:46:59ZengMDPI AGSensors1424-82202022-02-01225183710.3390/s22051837A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving OutlookAbdullah Alqahtani0Frederick T. Sheldon1Department of Computer Science, University of Idaho, Moscow, ID 83843, USADepartment of Computer Science, University of Idaho, Moscow, ID 83843, USARecently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope.https://www.mdpi.com/1424-8220/22/5/1837crypto ransomwaredata centricprocess centricevent-based detectionearly detectiondeep learning |
| spellingShingle | Abdullah Alqahtani Frederick T. Sheldon A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook Sensors crypto ransomware data centric process centric event-based detection early detection deep learning |
| title | A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook |
| title_full | A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook |
| title_fullStr | A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook |
| title_full_unstemmed | A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook |
| title_short | A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook |
| title_sort | survey of crypto ransomware attack detection methodologies an evolving outlook |
| topic | crypto ransomware data centric process centric event-based detection early detection deep learning |
| url | https://www.mdpi.com/1424-8220/22/5/1837 |
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