Enterprise Malware Detection & Classification Engine

This enterprise-grade security analytics platform leverages supervised machine learning to classify and intercept endpoint malware threats in real-time.

Traditional signature-based antivirus solutions are chronically slow to respond to zero-day threats. Security operation centers require heuristic, ML-driven models that can identify anomalous behavior and classify polymorphic malware instantly.

I built a scalable Python ML pipeline that ingests raw executable metadata and dynamic behavioral logs. By automating feature extraction using Pandas, the system processes massive threat corpora to continuously train and evaluate classification models using Scikit-learn.

Automated feature engineering — Designed a robust extraction layer that strips highly predictive structural features from PE (Portable Executable) files without requiring sandbox execution.

Continuous retraining loops — Structured the architecture to natively support automated model retraining as new threat data is ingested, preventing model drift.

The resulting classification engine demonstrated exceptionally high accuracy rates against a diverse dataset of known and zero-day malware, providing a reliable proof-of-concept for ML-augmented endpoint security.