Blockchain‐Based Framework for Secure Cloud Data Encryption Using Heterogeneous Bi‐Directional Recurrent Neural Network

ABSTRACT As more individuals use public cloud networks for data storage and handling, providing the security, integrity, and confidentiality of private data becomes essential. Classical encryption solutions are unable to address the growing threats and large‐scale data processing required in such environments. Therefore, a Blockchain‐Based Framework for Secure Cloud Data Encryption Using Heterogeneous Bi‐Directional Recurrent Neural Network (BCF‐SCDE‐HBDRNN) is proposed in this paper. The input data is gathered from the IDS 2018 Intrusion CSVs Dataset. Initially, the data is encrypted using the Martino Homomorphic Encryption Algorithm (MHEA) for data security. The data is then secured using the Fair Proof‐of‐Reputation Consensus Algorithm (FPoR) based on blockchain technology for secure data storage. A Multi‐Agent Cubature Kalman Optimizer (MACKO) is employed for optimal key management. Finally, Heterogeneous Bi‐Directional Recurrent Neural Networks (HBDRNN) are used for threat detection such as normal and attack. Experimental evaluation demonstrates that the proposed framework enhances encryption efficiency, strengthens key management, and provides highly reliable threat detection compared to existing methods. The overall results highlight the framework's effectiveness as a robust and scalable solution for secure cloud data protection.