Efficient malware detection based on machine learning for enhanced cloud privacy protection

Cloud computing environments are increasingly popular due to their flexibility and scalability, but they also present significant security challenges, particularly in the form of malware attacks. These malicious attacks exploit weaknesses within cloud infrastructures, which can result in serious repercussions like data breaches, unauthorized system access, and identity theft. In this paper, we introduce an innovative malware detection classifier specifically designed to overcome the shortcomings of conventional machine learning algorithms, such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), in the unique context of cloud environments. Our proposed method relies on Log-spectral distance as a fundamental metric, which enables a more precise and effective approach to detecting malware. Through rigorous and extensive experimentation, our findings demonstrate that this novel classifier achieves an outstanding accuracy rate of 97% without the need for feature selection-surpassing the 95% accuracy attained when employing feature selection through the Mutual Information (MI) method. Additionally, our classifier outperforms both traditional machine learning (ML) and deep learning (DL) techniques, showcasing its robustness and dependability in identifying malware threats within cloud settings. The results of our study underscore the classifier's potential to serve as a crucial tool for enriching security in cloud environments. This advanced solution not only contributes to academic research but also offers practical applications for safeguarding cloud infrastructures against the continuously evolving landscape of malware threats.