Machine Learning-Based Static Analysis for Malware Detection in Executable Files

Author(s)	Ms. Nenavath Vasantha, Dr. Kotoju Rajitha, Mr. Marapaka Varshik
Country	India
Abstract	The increasing threat of malware in the digital world necessitates robust and scalable detection systems. This paper introduces a machine learning-based malware detection system that analyzes Portable Executable (PE) files to identify malicious software. Leveraging supervised learning algorithms and feature engineering, the system achieves high accuracy in detecting harmful binaries. The Random Forest classifier, trained on a large dataset of PE files, demonstrated exceptional performance. The proposed system streamlines malware classification and can be integrated into broader cybersecurity frameworks.
Keywords	Malware Detection, Portable Executable, Random Forest, Feature Extraction, Cybersecurity, PE File Analysis
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-29
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.43163
Short DOI	https://doi.org/g9g76c

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International Journal For Multidisciplinary Research