Fusion Models for Cyber Threat Defense: Integrating Clustering with Kmeans, Random Forests, and SVM against Windows Malware

Innovative defensive techniques are needed to address threats caused by Windows malware that are becoming more prevalent. This study presents a novel method for thwarting the complex web of cyberattacks by combining the powers of clustering, Random Forests (RF), and Support Vector Machines (SVM). Our fusion model performs remarkably well in the field of Windows malware security, with an accuracy of 98.67% and similarly F1 score of 98.68%. Our methodology transforms cybersecurity readiness by breaking down the complex patterns seen in malicious code, hence improving detection accuracy. This innovation highlights the possibilities found in the merging multiple analytical methods.