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

Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern. All traditional security mechanisms have central vulnerabilities, cannot detect real-time threats, and are ineffective against zero-day attack...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Scientific reports Ročník 15; číslo 1; s. 38711 - 21
Hlavní autori: Nagarjun, A. Venkata, Rajkumar, Sujatha
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 05.11.2025
Nature Publishing Group
Nature Portfolio
Predmet:
639/166
639/705
Access control
Accuracy
Adaptability
Adaptive intrusion detection
Blockchain
Cloud computing
Cloud data transfer
Confidentiality
Data integrity
Deep learning
Ethereum blockchain security
Fraud
Fraud prevention
Humanities and Social Sciences
Intrusion detection systems
multidisciplinary
Neural networks
Quantum autoencoder
Science
Science (multidisciplinary)
Smart contracts
Threats
Training
Adaptive intrusion detection
Deep learning
Cloud data transfer
Smart contracts
Ethereum blockchain security
Quantum autoencoder
ISSN:2045-2322, 2045-2322
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Because of the rapid acceleration of cloud computing, data transfer security and intrusion detection in cloud networks have become emerging areas of concern. All traditional security mechanisms have central vulnerabilities, cannot detect real-time threats, and are ineffective against zero-day attacks. Signature-based approaches of existing intrusion detection systems (IDS) do not cover the dynamically changing nature of cyber threats. Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis. Therefore, this research proposes integrating Ethereum Blockchain and Deep Learning to construct a well-founded security framework for cloud networks with data migration security and real-time intrusion detection. The architecture has five distinct methods, each of which deals with particular security issues. Blockchain-Aware Federated Learning for Secure Model Training (BAFL SMT) guarantees tamper-proof and decentralized deep learning model training, which reduces model poisoning attacks by 98.4%. Graph Neural Networks for Adaptive Intrusion Detection (GNN-AID) captures graph structures for real-time anomaly detection in networks while reducing false positives to 1.2%. Quantum-inspired Variational Autoencoders (QI VAE ZDAD) provide enhanced zero-day attack detection, with an improved detection rate of 92%. Self-Supervised Contrastive Learning for Blockchain Security Auditing (SSCL-BSA) detects smart contract vulnerabilities automatically, resulting in an 87% reduction in fraud risk. Finally, Hierarchical Transformers for Secure Data Migration (HT SDM) enhance the transfer security of large-scale cloud data, achieving an attack classification accuracy of 99.1%. Overall, this multi-layer security framework will greatly enhance cloud security by preserving data integrity, cutting down the intrusion detection time by up to 65%, and enhancing response mechanisms. By marrying the immutable transparency of blockchain with superior anomaly detection at deep learning, this research provides a scalable, real-time, and intelligent approach to strengthening security against the backed-up transfer of data within cloud networks.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-22408-1