Quantum deep learning-enhanced ethereum blockchain for cloud security: intrusion detection, fraud prevention, and...

Area of Science:

• Cybersecurity
• Cloud Computing Security
• Applied Artificial Intelligence

Background:

• Cloud computing's rapid growth necessitates advanced security measures.
• Traditional security lacks real-time threat detection and zero-day attack defense.
• Existing intrusion detection systems (IDS) and blockchain methods face scalability and dynamic threat analysis challenges.

Purpose of the Study:

• To develop a robust security framework for cloud networks using Ethereum Blockchain and Deep Learning.
• To enhance data migration security and enable real-time intrusion detection.
• To address limitations of traditional security mechanisms against evolving cyber threats.

Main Methods:

• Blockchain-Aware Federated Learning for Secure Model Training (BAFL SMT) for decentralized model training.
• Graph Neural Networks for Adaptive Intrusion Detection (GNN-AID) for real-time anomaly detection.
• Quantum-inspired Variational Autoencoders (QI VAE ZDAD) for zero-day attack detection.
• Self-Supervised Contrastive Learning for Blockchain Security Auditing (SSCL-BSA) for smart contract vulnerability detection.
• Hierarchical Transformers for Secure Data Migration (HT SDM) for secure large-scale data transfer.

Main Results:

• BAFL SMT reduced model poisoning attacks by 98.4%.
• GNN-AID reduced false positives to 1.2%.
• QI VAE ZDAD achieved a 92% zero-day attack detection rate.
• SSCL-BSA reduced fraud risk by 87%.
• HT SDM achieved 99.1% attack classification accuracy.
• Overall intrusion detection time reduced by up to 65%.

Conclusions:

• The integrated framework enhances cloud security, data integrity, and response mechanisms.
• Combines blockchain's transparency with deep learning's anomaly detection for scalable, real-time security.
• Provides an intelligent approach to secure data transfer within cloud networks against sophisticated threats.