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The proposed architecture uses data preparation, feature extraction, Random Forest classification and K-Means clustering for cloud malware detection. The hybrid approach detects known and emerging threats and achieves 98.14% accuracy while protecting user privacy.
Cloud computing enables portable access to data and services but introduces significant malware and security risks, including data theft, compromised credentials, system damage and identity theft.