An Efficient Framework for Multi-Class Malware Classification in Cloud Environments

Malware classification in cloudenvironments remains a critical challenge due to theincreasing complexity and volume of cyber threats.This paper proposes CMC (Cloud-based MalwareClassification), a novel framework that enhancesmulti-class malware classification efficiency throughthe integration of feature selection, dimensionalityreduction, and imbalanced data handlingtechniques. The CMC framework aims to improveclassification accuracy and computational efficiencyby optimizing feature representation and addressingclass imbalance, which are common issues inreal-world malware datasets. To evaluate itseffectiveness, we apply the proposed model to twopublic benchmark datasets: CMD_2024 andCIC-MalMem-2022. Experimental resultsdemonstrate that CMC outperforms existingapproaches in terms of classification accuracy,F1-score, and computational efficiency, proving itspotential for real-world deployment in cloud-basedsecurity solutions. These findings highlight theimportance of intelligent data preprocessing andfeature optimization in enhancing malwareclassification on cloud platforms.