*7.2. Future Work*

Future work in machine-learning-based ransomware detection could include the following:


#### **8. Conclusions**

Ransomware attacks have caused significant harm to computer systems and the data they manage, resulting in unauthorized access, disclosure, and the destruction of important and sensitive information. These attacks have led to substantial financial losses and reputational damage for both individuals and businesses. In response, various methods have been suggested to detect ransomware accurately, quickly, and dependably. This research provides readers with a historical background and timeline of ransomware attacks, as well as a discussion of the issue's context. The review of the recent literature offers an upto-date understanding of automated ransomware-detection approaches. This knowledge will help readers stay current on the latest advances in automated ransomware detection, prevention, mitigation, and recovery. Additionally, this research discusses future research directions, highlighting open issues and potential research problems for those interested in researching ransomware detection, prevention, mitigation, and recovery.

**Author Contributions:** Author Contributions: collecting the papers, A.A. (Amjad Alraizza); Formal analysis, A.A. (Amjad Alraizza); Resources, A.A. (Abdulmohsen Algarni); Writing—original draft, A.A. (Amjad Alraizza); Writing review and editing, A.A. (Abdulmohsen Algarni); Supervision, A.A. (Abdulmohsen Algarni); Funding acquisition, A.A. (Abdulmohsen Algarni). All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was financially supported by the Deanship of Scientific Research at King Khalid University under research grant number (R.G.P.2/549/44).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
