Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines

TL;DR

This paper develops advanced machine learning models using real-world Windows Defender data to predict malware vulnerabilities, aiming to improve proactive cybersecurity measures beyond traditional detection methods.

Contribution

It introduces sophisticated ML algorithms with feature engineering on large datasets, enhancing malware detection and providing adaptable models for enterprise cybersecurity.

Findings

Improved malware vulnerability prediction accuracy

Identification of key malware indicators through feature analysis

Models applicable to large-scale enterprise environments

Abstract

In an era of escalating cyber threats, malware poses significant risks to individuals and organizations, potentially leading to data breaches, system failures, and substantial financial losses. This study addresses the urgent need for effective malware detection strategies by leveraging Machine Learning (ML) techniques on extensive datasets collected from Microsoft Windows Defender. Our research aims to develop an advanced ML model that accurately predicts malware vulnerabilities based on the specific conditions of individual machines. Moving beyond traditional signature-based detection methods, we incorporate historical data and innovative feature engineering to enhance detection capabilities. This study makes several contributions: first, it advances existing malware detection techniques by employing sophisticated ML algorithms; second, it utilizes a large-scale, real-world dataset to ensure the applicability of findings; third, it highlights the importance of feature analysis in identifying key indicators of malware infections; and fourth, it proposes models that can be adapted for enterprise environments, offering a proactive approach to safeguarding extensive networks against emerging threats. We aim to improve cybersecurity resilience, providing critical insights for practitioners in the field and addressing the evolving challenges posed by malware in a digital landscape. Finally, discussions on results, insights, and conclusions are presented.