Build a Malware Detection System Using Machine Learning

Traditional malware detection relies on known signatures, which fail against new and evolving threats. Machine learning models can generalize patterns from known malware and detect zero-day variants by analyzing behavioral traits and static features — making them a powerful defense mechanism.

The system uses a dataset of labeled benign and malicious files. It extracts features like file size, entropy, imported libraries, permissions, and API call sequences, then trains a classifier model to predict if a file or process is malicious.

After training a model on known malware datasets, the system accepts a file as input, extracts features, and classifies it. The result is presented along with a confidence score and highlights of risky patterns or behaviors identified by the model.

• Collect and preprocess labeled malware and benign samples.
• Extract relevant features from files (static or behavioral).
• Train and validate a machine learning classifier.
• Deploy the model in an API or UI for real-time scanning.
• Return a verdict (malicious/benign) with reasoning.

• EMBER Malware Dataset
• Scikit-learn Documentation
• PEfile for Parsing EXE Headers
• XGBoost Python Guide
• Streamlit for ML Apps