ANALYSIS AND COMPARISON OF MACHINE LEARNING METHODS FOR MALWARE DETECTION

Our study aims to analyze and evaluate modern machine learning methods for detecting malware, a critical challenge given the increasing complexity and volume of cyber threats. We explore various algorithms, including Support Vector Machines, Random Forest, Logistic Regression, Decision Trees, and K-Means Clustering, comparing their effectiveness in identifying and classifying malware. Our methodology combines static, dynamic, and memory-based analysis techniques, offering a comprehensive approach to understanding malware behavior. Key findings reveal that Decision Trees and Random Forests demonstrate impressive accuracy in both binary and multi-class classification tasks. We also highlight novel methods such as the Self-Organizing Incremental Neural Network (SOINN), which effectively handles evolving malware threats. The integration of static and dynamic analysis methods deepens insights into malware behavior. This research underscores the importance of advancing machine learning techniques to enhance cybersecurity measures against evolving global malware threats, offering valuable insights for future research directions.