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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM34343

Ransomware Threat Analysis Using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM34343

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM34343

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DT

Dr.Anitha T.N

AKM

Ashish Kumar Mahto

HP

Harsh Pandey

SP

Sakshi Pandey

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Abstract

With the advancement and easy accessibility of computer and internet technology, network security has become vulnerable to hacker threats, including ransomware attacks targeting smartphone operating systems (e.g., Android) and applications. Despite efforts to detect malicious URLs, lexical features sometimes need to be improved. This document provides an overview of ransomware, a timeline of assaults, and comprehensive research on methods for identifying, avoiding, minimising, and recovering from ransomware attacks. Analysing studies between 2017 and 2024 offers up-to- date knowledge on developments in ransomware detection and advancements in combating ransomware attacks, highlighting unanswered concerns and potential research challenges.

KEYWORDS

Machine Learning Ransomware Cybers-attacks Android Malware Hacker Ransomware detection.

Cite This Article

Dr.Anitha T.N, Ashish Kumar Mahto, Harsh Pandey, Sakshi Pandey, "Ransomware Threat Analysis Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM34343

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Publication Date

12/31/45432

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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