Machine Learning for Malware Detection

The rapid growth of digital technologies and internet connectivity has significantly increased the risk of cyber threats, particularly malware attacks. Traditional malware detection methods mainly rely on signature-based techniques, which are often ineffective against newly emerging and polymorphic malware. To address these limitations, machine learning–based malware detection has emerged as a powerful approach for identifying malicious software by analyzing patterns and behavioral characteristics.

This study presents a machine learning–based framework for malware detection that analyzes features extracted from executable files and system behaviors. The proposed approach involves data collection, feature extraction, feature selection, and the application of classification algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks. These algorithms are trained to distinguish between benign and malicious software by learning from historical datasets.