Ransomware Detection in Cloud Environments Using Deep Learning Techniques

The rapid adoption of cloud computing has transformed the digital infrastructure of modern organizations, offering scalable, on-demand services. However, this transformation has also attracted sophisticated cyber threats, particularly ransomware attacks that encrypt or exfiltrate critical data and demand ransom payments. Traditional signature-based detection methods struggle to identify emerging and polymorphic ransomware variants, especially within dynamic and virtualized cloud environments. To address this challenge, deep learning techniques have emerged as promising solutions due to their ability to automatically learn complex data patterns and generalize across diverse threat landscapes. This paper explores the application of deep learning models—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid architectures—for accurate and real-time ransomware detection in cloud environments. We propose a detection framework that leverages behavioral data (such as API calls, file system changes, and network traffic) extracted from cloud instances and applies advanced neural networks to identify malicious activities. Experimental results, based on public and synthetic datasets, demonstrate the effectiveness of deep learning in achieving high detection accuracy, low false positive rates, and adaptability to evolving attack strategies. The paper also discusses deployment challenges, privacy implications, and potential strategies for integrating these models into cloud security architectures. Our findings highlight the potential of AI-driven security frameworks in building resilient cloud ecosystems capable of proactively defending against ransomware threats.