Published Paper Details:

Malware is an increasing threat to cybersecurity, causing significant harm to systems, data, and networks worldwide. As cyberattacks become more sophisticated, it's crucial to develop reliable and effective methods for detecting and preventing malware. This project addresses this issue by using the Random Forest algorithm, a well-established machine learning technique known for its ability to handle complex datasets and deliver high accuracy. The model is trained to analyse features extracted from executable files, such as file size, code structure, and patterns in behaviour, to distinguish between benign and malicious software. One of the key advantages of the Random Forest approach is its ability to adapt to new and evolving types of malware, ensuring it stays effective as threats change. Additionally, its resistance to overfitting and scalability make it a robust choice for detecting even the most sophisticated malware. Ultimately, this project aims to enhance cybersecurity by providing a flexible, precise, and scalable solution for malware detection.