PE Defender - Malware Detection Model Using Machine Learning

PE Defender presents a machine learning-based approach for detecting malware in Portable Executable (PE) files. The system extracts structural and behavioral features from PE headers, sections, and imported functions and uses them to classify files as malicious or benign. Multiple machine learning algorithms, including Decision Trees, Random Forests, Support Vector Machines (SVM), and Gradient Boosting, are evaluated for malware detection. The results demonstrate the potential of machine learning-based approaches for identifying malicious PE files, including previously unknown threats, while reducing reliance on traditional signature-based detection techniques.