As the sophistication and volume of malware threats continue to escalate, we need automated solutions for accurate identification and categorization. This thesis aims to design and develop an automated malware classification system using machine learning algorithms to distinguish between various malware types and propose a methodology for continuous learning as new malware variants emerge.
This project focuses on all or some of the following:
- To develop an automated malware classification system using machine learning.
- To evaluate the system's performance in real-time scenarios and against existing classification systems.
- To propose a methodology for updating the machine learning model to recognize new malware variants.
- Selection of suitable machine learning algorithms for malware classification.
- Training, validation, and testing of the ML models.
This project is in collaboration with the national police.
