Jun 2026· 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS)· pp. 1483-1490· 0 citations· 26 references
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.
This paper introduces Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework and formalizes privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats.
The rapid development of the Internet of Things (IoT) has placed considerable pressure on both security and stability in heterogeneous, resource-constrained networks. In such dynamic environments, trust management is a central issue to determine which service providers can be trusted and to combat malicious activity. Although blockchain-based solutions have offered a means for decentralized, tamper-resistant trust management, most rely on classical cryptographic primitives, which are vulnerable to future quantum computing attacks. This study proposes a Quantum-Resistant Blockchain-Based Trust Management (QR-BCTM) framework in which Post-Quantum Cryptographic mechanisms, Permissioned Blockchain Platform, and Fog-assisted Trust Management architecture are combined and utilized in IoT networks. The framework introduces a quantum-aware trust computation model that combines behavioral trust, indirect recommendations, and a cryptographic assurance score quantifying each participant’s compliance with security requirements. Trust evidence is compressed to reduce blockchain storage and communication overhead, while the hierarchical fog-blockchain architecture offloads computationally intensive operations from resource-constrained IoT devices. The performance of the framework has been simulated in the presence of an adversary, including bad-mouthing, ballot-stuffing, on-off behavior, and identity attacks using a Sybil-type mechanism. Trust accuracy, false trust acceptance, communication overhead, and computation cost were measured, and a sensitivity analysis on the trust-weight parameters was performed. The simulation results suggest that QR-BCTM can enhance the accuracy of trust evaluation, mitigate the impact of malicious nodes, and remain scalable and efficient despite the existing cryptographic overhead. Post-quantum digital signatures and formal security analysis provide protection against quantum-era threats and attacks, while classical threats are mitigated through behavioral trust aggregation and recommendation filtering. In summary, QR-BCTM provides a scalable, simulation-validated and quantum-aware framework for trustworthy IoT network operation, offering practical guidelines for future deployment and prototyping.
M. A. Al-Khasawneh, D. Alsekait, K. Alkayid et al.· Scientific Reports· 0 citations
Cloud computing has become the fundamental technology for modern digital infrastructure supporting many business activities and the organization of the internet [1] because its promises of scalability, flexibility, and efficient own resource utilization were highly considered by many organizations. Nevertheless, these advantages come with a security challenge and cloud environments can be threatened by an even broader list, including unauthorized accesses, data leakages, privacy breaches, and multiple other forms of cyberattacks. In fact, one could argue that traditional security solutions require enhancement when sensitive data is processed by distributed cloud systems, and specifically at the scale of those systems. We propose a secure cloud computing scheme based on blockchain technology and DL based intrusion detection with the purpose to realize privacy-preserving cloud data management. The proposed scheme aims at including decentral-based authentication mechanism, encrypted communication procedure, and an efficient anomaly detection module to enhance the cloud security and reliability as a system. The experiment results prove that the proposed scheme owns better attack detection accuracy, secure data sharing and less security holes than traditional cloud-security schemes. All in all, this architectural framework may yield promising results in security cloud infrastructures for healthcare, and financial system as well as more intelligent enterprise(ies) in general, particularly where privacy and trust are of the strongest hold.
Soujenya.voggu, Devarapu Shiva Prasad, Pasam Ramu et al.· International journal of com...· 0 citations
Secure and transparent system for recording and verifying digital transactions across distributed networks. Distributed blockchain consensus is achieved through decentralized protocol rules, cryptographic authentication mechanisms, and scalable energy-efficient operations. The present study applies Quantum Mayfly Optimization (QMFO) within a blockchain-based collaborative intrusion detection framework. Collaborative intrusion detection systems (CIDS) have certainly carved their valued place in enhancing modern cybersecurity in the complex landscape of cyber threats. What the BCIDF brings into the picture is a new radical avenue to enhance the detection of new threats and information sharing. In this respect, the proposal cohesively combines distributed blockchain technology and collaborative intrusion detection to increase security, transparency, and trust within cyber realms. Fine-tuning the model parameters will improve blockchain classification accuracy and efficiency, and O(QMFO), a bio-inspired hybrid algorithm inspired by the principles of quantum leaf-edge swarm behavior, is directed toward ensuring the security and performance of blockchain networks. Quantum Mayfly optimization (QMFO) and a Blockchain-based Collaborative Intrusion Detection Framework (BCIDF) are designed to secure distributed networks by allowing tamper-resistant sharing of alerts in the case of an attack across the blockchain. The term 'Quantum Mayfly Optimizer (QMFO)' here is used to amplify performance, speed, and accuracy. Integration, therefore, guarantees the best detection and few false positives, and ensures adaptive actions against upcoming threats.
M. Savitha, I. P. Stella Mary, A.Manikandan et al.· 2026 6th International Confe...· 0 citations
The exponential growth of cloud computing has enabled large-scale data outsourcing but has simultaneously introduced critical challenges related to data confidentiality, integrity, and trust. Traditional cryptographic and blockchain-based cloud security solutions often suffer from high computational overhead, latency, and scalability limitations, which hinder their practical adoption. To address these issues, this study proposes a robust and lightweight blockchain-based security framework for secure cloud data storage. The framework integrates hybrid AES–ECC encryption, smart contract–driven access control, and a lightweight consensus mechanism combining Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve efficient and tamper-resistant data management. The proposed system employs an on-chain/off-chain hybrid architecture that stores only essential metadata and cryptographic proofs on the blockchain while maintaining the actual data in distributed cloud storage. This design minimizes computational burden and blockchain bloat while ensuring end-to-end transparency and verifiability. A Merkle tree–based Proof of Storage (PoS) mechanism enables rapid integrity verification without requiring full data retrieval. Comprehensive experiments were conducted using a simulated multi-node cloud environment to evaluate encryption efficiency, transaction latency, throughput, storage overhead, and energy consumption. Results show that the proposed framework outperforms existing blockchain-based models, achieving a 37.7% reduction in encryption/decryption time, a 51.3% decrease in transaction latency, and a 54.5% improvement in energy efficiency. Additionally, the system attained a 99.3% security success rate under various attack scenarios, demonstrating its resilience against unauthorized access, replay, and tampering attempts. These findings confirm that the proposed approach provides a practical balance between security assurance and performance optimization.
R. Bala, S. Gnanavel· International Journal of Adv...· 0 citations
The increasing adoption of cloud computing, the Internet of Things (IoT), and distributed networks has intensified
cybersecurity challenges, requiring intelligent, secure, and privacy-preserving threat detection mechanisms. This paper proposes
a hybrid framework that integrates Explainable Artificial Intelligence (XAI), Federated Learning (FL), and Blockchain to
develop a trustworthy cybersecurity system. Federated Learning enables collaborative model training without sharing sensitive
data, blockchain ensures secure and tamper-resistant verification of model updates, and XAI techniques such as SHAP and
LIME provide transparent explanations for cyber threat predictions. The proposed framework is evaluated using benchmark
intrusion detection datasets based on metrics including accuracy, precision, recall, F1-score, blockchain latency, and
communication overhead. The results demonstrate improved threat detection performance, enhanced privacy preservation,
greater transparency, and stronger resilience against adversarial attacks. The proposed hybrid framework offers an effective
solution for secure and trustworthy next-generation cybersecurity in IoT, cloud, and enterprise environments.
Dr. Ravi Chinkera, Daem Pasha· International Journal for Re...· 0 citations