QB-HHO: Enhancing Data Integrity and Secure Communication in Distributed Clouds

Abstract

As distributed cloud infrastructures grow, the demand for secure, decentralized, and resilient communication solutions with the ability to safeguard sensitive data against growing quantum computing attacks and advanced cyber threats has increased. In response to these challenges, this research introduces a Quantum-Inspired Blockchain-Assisted Harris Hawk Optimization (QB-HHO) algorithm that establishes a hybrid decentralized security architecture that combines quantum-resistant cryptographic techniques with blockchain-based trust management. The proposed framework is implemented and evaluated in the CloudSim Plus simulator using the Google Cluster Workload Trace dataset, enabling realistic assessment of large-scale cloud environments. QBHHO optimally selects secure communication paths, dynamically manages trust scores, and increases the efficiency of the blockchain consensus while ensuring data confidentiality and integrity. A comparison of proposed approach with Multi-Agent Deep Learning (MADL), Enhanced Convolutional Temporal Network (EnCTN), and Blockchain-enabled Security Architecture for Fog Networking Environment (B-SAFE) is performed to verify the performance of the proposed approach. Experimental results show that QB-HHO provides better security and network performance with ${9 9. 2 1 \%}$ Data Integrity, 98.84% Confidentiality, 97.63% Packet Delivery Ratio (PDR), 1.82 s Link Failure Recovery Time and 96.75% Network Lifetime. In addition, QB-HHO enhances Data Integrity (8.94%), Confidentiality (10.37%), PDR (9.12%) and Network Lifetime (12.46%) over the existing methods and reduces Link Failure Recovery Time by 36.58% over the existing methods. The findings demonstrate that the proposed QB-HHO framework offers a comprehensive, scalable, and sustainable security paradigm for decentralized cloud systems, guaranteeing secure communication, tamper resistance, and resilience to traditional and quantum threats.