Privacy-Aware Ransomware Detection in Cloud Environments Using a Hybrid ReLU- GRU, Adaptive Ant Colony Optimization, and Shannon Entropy Framework

Ransomware attacks in cloud environments for data storage have an increased concern that demands ransom payments that encrypt critical data. The proposed method is classified based on Shannon Entropy, ReLU-GRU networks and adaptive Ant Colony Optimization (ACO) has been used to enhance the proposed method. The encrypted files are detected utilizing Shanon entropy with file randomness while the sequential data patterns are captured by the ReLU-GRU for the emerging attack patterns for the precise ransom ware classification. The framework is validated using a dataset of 6,245 records , distinguishing ransom ware from benign activity based on system logs, API calls, and process behaviors. Pre-processing techniques, including data normalization, feature extraction, and privacy-preserving API security , refine input data. Results demonstrate 99.13% accuracy, 99.11% precision, 99.07% recall, and a 99.09% F1-score , surpassing methods like SAE-LSTM, GAN, and RLDAC . Shannon Entropy-based API security further prevents unauthorized access. The ROC curve confirms a perfect AUC score of 1.00 , while the model achieves a 99.1% detection rate with a false positive rate of just 0.81% , outperforming techniques like SMO and FeSA . The proposed ReLU-GRU + ACO + Shannon Entropy model proves to be efficient, scalable, and privacy-aware . Future research will explore federated learning for distributed detection and zero-trust security architectures to counter advanced ransomware threats.