Machine Learning Based Malware Detection Using Static File Analysis Techniques

The objective of this project is to develop a malware detection system using machine learning techniques based on static file analysis. The system analyzes executable files without running them and classifies them as malicious or benign based on extracted features and patterns.

Study fundamentals of malware types such as viruses, worms, trojans, ransomware, and spyware.

Understand differences between static and dynamic malware analysis techniques.

Collect a dataset of benign and malicious executable files from publicly available repositories.

Extract static features such as file size, header information, imported libraries, strings, and byte sequences.

Perform feature preprocessing including normalization and dimensionality reduction.

Implement machine learning algorithms such as Decision Tree, Random Forest, or Support Vector Machine for classification.

Train and test the model using appropriate data splitting techniques.

Evaluate system performance using accuracy, precision, recall, and confusion matrix.

Develop a user interface that allows users to upload files for malware scanning.

Document model limitations and propose improvements for higher detection accuracy.