A terminal based program which detects whether a URL or .exe file is malicious or not.
This project implements a terminal-based application designed to detect malicious files and URLs using machine learning techniques. Its key features are:
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Malware/Benign File Classification: Utilized Random Forest classification to accurately categorize files as either malware or benign. This approach enhances detection capabilities by leveraging the ensemble learning technique for better classification performance.
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Feature Extraction: Conducted detailed feature extraction from Portable Executable (PE) header files to enrich the dataset and improve the effectiveness of the classification model.
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URL-Based Detection: Applied Logistic Regression to build a model for identifying potentially harmful URLs. This method improves the program's ability to detect and flag suspicious web links.
This program combines these techniques to provide a robust solution for detecting and mitigating potential security threats.
