Malicious Application Detection in Windows Using Machine Learning

Abstract

As the proliferation of digital technology continues, the threat landscape for Windows operating systems has become increasingly complex. Malicious applications, including viruses, ransomware, and spyware, pose a significant risk to both individuals and organizations. To combat this growing threat, there is a pressing need for effective and efficient methods for detecting and mitigating malicious applications. This research paper presents an innovative approach to Malicious Application Detection in Windows using Support Vector Machine (SVM) algorithms. SVM is a powerful machine learning technique that has been successfully applied in various classification tasks, including malware detection. The primary objective of this study is to develop a robust and reliable system that can differentiate between benign and malicious applications in a Windows environment. We start by collecting a comprehensive dataset of Windows applications, comprising both legitimate and malicious software samples. Feature extraction techniques are employed to convert the application data into a suitable format for SVM analysis. These features may include file attributes, system call sequences, and behaviour analysis metrics