Analyzing Ransomware attack & improving Detection Rate

Author(s)	Mr. Mohammad Sharay Ammar
Country	India
Abstract	The purpose is to explore and analyse various malware detection approaches, as well as to propose a strategy to decrease false positives when separating ransomware attack files from benign (non-malicious) files. The goal of this study is to train a model that can detect any unknown file, that is not in the virustotal database, with high accuracy without any external assistance. Finally, we will cross-check with the Virustotal API for that specific Hash value to see whether it can identify it with a fair Threshold value. The resulting accuracy of 95.62 percent demonstrates the method's efficiency in detecting malware. This paper aims to collect data that will aid in filling research gaps in machine learning-based malware.
Keywords	Hashing, encryption, Machine Learning, Ransomware, API Call Sequencing, Malware analysis.
Field	Computer > Data / Information
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-08-13
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.53664
Short DOI	https://doi.org/g9w7gz

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