Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach

Abstract

Abstract: One of the most popular OSs utilized by the public these days is Windows. A serious concern to the security and integrity of Windows OS systems is the proliferation of malware. The goal of this research project is to create a practical method for identifying and categorizing various malware kinds on the Windows operating system to combat the pervasive malware problem. For the efficient identification and classification of malware on Windows, an ensemble technique using hybridization of Support Vector Machine, Decision tree, and Logistic Regression is proposed. The suggested method makes use of the idea of feature selection methods to determine the patterns and signatures of numerous malware families. The genuine malware dataset will be used to test and assess the suggested ensemble as well as the current basic machine learning techniques. In the end, this will assist both the novice and the expert in cyber security to comprehend and prepare for the ever-changing threats posed by the new breed of malware on Windows PCs.

Key words: malware classification, machine learning, information security, security analysis, windows os

Vikas Verma, Arun Malik, and Isha Batra. Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach [J]. Int J Performability Eng, 2024, 20(5): 312-318.

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