International Journal of Leading Research Publication

E-ISSN: 2582-8010   •   Impact Factor: 9.56

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 8 August 2025 Submit your research before last 3 days of to publish your research paper in the issue of August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 8
August 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Mitigating Ransomware Threats: A Machine Learning Perspective on Dynamic Feature Utilization

Author(s) Meenakshi Jalandra, Megha Kuliha, Jasmeet Kaur
Country India
Abstract In a review of the literature on detecting and averting ransomware attacks, this study focuses on current studies that were published between 2020 and 2025. Although the quick development of digital technology has brought about many conveniences, the threat of ransomware—a type of malware that encrypts the victim's files and then demands a large ransom to unlock them—is also growing. Cybersecurity is generally defined as safeguarding systems against all cyberattacks. Here, we know that ransomware is a method of stealing money from a user, where the attacker encrypts the user's data and retains the decryption key until he is paid the ransom. In this study, we examine a number of scholarly works that include ransomware detection strategies, indicators, tactics, approaches based on URL characteristics, and efficient machine learning models. This study takes into account new patterns in the accuracy of models from URL datasets that come from different machine-learning techniques as well as novel methods that use diverse models to identify ransomware. The results highlight the limitations of existing research and the application of robust models to develop cybersecurity hybrid models. Future research aims to choose appropriate models for identifying URL-based ransomware on Android smartphones based on this review. Even if there are still fresh, evident problems and restrictions with different detection methods, this article also emphasizes the necessity of constant development in the constantly evolving URL-based ransomware detection strategies.
Keywords Ransomware Detection, Machine Learning, Dynamic Features, Cybersecurity, Light GBM, Random Forest, Behavioural Analysis, Malware Detection ,Android Ransomware
Field Engineering
Published In Volume 6, Issue 7, July 2025
Published On 2025-07-26
Cite This Mitigating Ransomware Threats: A Machine Learning Perspective on Dynamic Feature Utilization - Meenakshi Jalandra, Megha Kuliha, Jasmeet Kaur - IJLRP Volume 6, Issue 7, July 2025.
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 7 Cover Page Vol 6 Isu 6 Cover Page Vol 6 Isu 5

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJLRP
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijlrp.com