Review
Version 1
Preserved in Portico This version is not peer-reviewed
Ransomware Detection using Machine Learning: Survey
Version 1
: Received: 22 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (10:09:59 CEST)
A peer-reviewed article of this Preprint also exists.
Alraizza, A.; Algarni, A. Ransomware Detection Using Machine Learning: A Survey. Big Data Cogn. Comput. 2023, 7, 143. Alraizza, A.; Algarni, A. Ransomware Detection Using Machine Learning: A Survey. Big Data Cogn. Comput. 2023, 7, 143.
Abstract
significant security threats to Ransomware attacks provide serious security hazards to personal and corporate data and information. The owners of computer-based resources suffer serious verification and privacy violations, monetary losses, and reputational damage due to a successful ransomware assault. As a result, it is reported critically, accurately, and swiftly identifying ransomware. Numerous methods have been proposed for ransomware, each with pros and cons. The main objective of this study is to discuss current trends and potential future debates on automated ransomware detection. The document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides a comprehensive study of existing methods for identifying, avoiding, minimizing, and recovering from ransomware. An analysis of studies between 2017 and 2022 is another advantage of the study. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection. It also highlights advancements in methods for combating ransomware attacks. In conclusion, this study highlights unanswered concerns and potential research challenges in ransomware detection.
Keywords
Machine Learning; Ransomware Techniques; Cybersecurity; Ransomware Detection; Ransomware Attacks
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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