Malware-classification-Using-ML

With the relentless evolution of the Internet, the threat of malware has become a pervasive concern in the cybersecurity landscape. This project addresses the limitations of traditional malware detection methods by leveraging machine learning techniques. The project focuses on two primary aspects: PE header-based malware detection and the identification of malicious URLs. The malware landscape is marked by its diversity and the diminishing skill level required for development, facilitated by the widespread availability of attack tools. Traditional signature�based methods, though effective against known threats, struggle with polymorphic viruses and fail to adapt to the dynamic nature of cybersecurity threats. This project introduces a machine learning model, employing the Random Forest Classifier, to enhance malware detection by recognizing patterns indicative of malicious behavior. In parallel, the project explores the realm of URL detection, recognizing the critical role of identifying malicious websites. Leveraging Logistic Regression and a custom Whitelist Filter, the model aims to distinguish between benign and malicious URLs. The integration of machine learning in both PE header-based and URL detection presents a holistic approach to fortifying cybersecurity defenses. The study evaluates the models using rigorous metrics, including ROC curves, confusion matrices, and correlation matrices, providing insights into their performance. The Random Forest Classifier exhibits an accuracy of 99.37%, highlighting its efficacy in handling polymorphic threats. The Logistic Regression model for URL detection achieves an accuracy of 98.46%, with a precision of 99.18% and a recall of 96.25%. The findings contribute to the ongoing discourse on advancing malware detection methodologies. Future enhancements could include a wider, well-labeled dataset, the development of a user�friendly graphical interface, and real-time scanning capabilities. The project underscores the potential of machine learning in fortifying cybersecurity measures and presents a foundation for continued research in this dynamic field