Device Threat Identification and Risk Assessment using Feature Analysis in Machine Learning

In response to the growing threat of malware in our digitally linked society, this research paper introduces a ground-breaking Malware Prediction System (MPS). Our system seeks to foresee and proactively detect malware in real-time using cutting-edge machine learning algorithms, serving as a critical defence mechanism against shifting cyber threats. The procedure comprises creating a user-friendly website that is accessible to both individuals and businesses. Users can rapidly upload files from their Windows computers, kicking off the feature analysis process. Based on the unique attributes acquired from the given files, the study estimates the likelihood of a malware attack using machine learning methods. Through a simple and intuitive interface, the system offers a smooth experience, allowing users to execute scans and obtain quick, actionable findings. Furthermore, the aim of the paper is to bring users with a tool that not only improves security but also contributes to cybersecurity awareness. By making the prediction process accessible and user-friendly, we aim to foster a proactive approach to malware prevention. The integration of Microsoft’s extensive dataset ensures the model’s robustness and adaptability to diverse cyber threats, making the MPS a valuable asset in the ongoing battle against malware.