Deep Belief Networks (Dbn) for Cloud-Based Malware Detection and Analysis

Cloud computing has increased the sophistication of cybersecurity threats, needing more advanced malware detection techniques. Traditional signature-based and heuristic approaches fail to keep up with the changing attack patterns. This study introduces a DBN-based solution for cloud-based malware detection and analysis. DBNs, a class of deep learning models, efficiently capture difficult feature demonstrations from large-scale data, generating them well-suited for categorizing innovative and polymorphic malware. Our method leverages cloud infrastructure for high-dimensional feature extraction and real-time analysis, improving detection accuracy while decreasing computational overhead on client systems. Experimental results found that our DBN-based model outperforms conventional machine learning classifiers in detection rate, false-positive reduction, and adaptability to emerging threats. This study highlights the potential of deep learning-driven solutions in improving cloud security and mitigating cyber threats in dynamic environments.