R-GCN: a residual-gated recurrent unit convolution network model for anomaly detection in blockchain transactions

The domain of deep learning has provided an exemplary paradigm for how Artificial Intelligence (AI) can be a disruptive technological paragon through Blockchain Technology (BT). Data experts have recently strived to find the quality of a dataset high enough for machine learning by an AI entity to be...

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Bibliographic Details
Published in:Multimedia tools and applications 2024-03, Vol.83 (40), p.87527-87551
Main Authors: Rajmohan, R., Kumar, T. Ananth, Sandhya, S. G., Hu, Yu-Chen
Format: Article
Language:English
Subjects:
Anomalies
Artificial intelligence
Blockchain
Computer Communication Networks
Computer Science
Convolution
Data Structures and Information Theory
Deep learning
Machine learning
Multimedia Information Systems
Smart grid
Special Purpose and Application-Based Systems
Unmanned aerial vehicles
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Summary:The domain of deep learning has provided an exemplary paradigm for how Artificial Intelligence (AI) can be a disruptive technological paragon through Blockchain Technology (BT). Data experts have recently strived to find the quality of a dataset high enough for machine learning by an AI entity to be effective and efficient. Blockchain technology has become a special, innovative, and fashionable technological development. It also guarantees that the data is reliable and valid through its consensus process. However, new protection creates problems like data anonymity and confidentiality. Deep Learning (DL)-based blockchain data security is needed to deal with the problems mentioned above. This paper proposes an integration of the DL and BT systems, which produces highly reliable performance in enhancing data durability and dissemination. Moreover, a new convolution model called Residual-Gated recurrent unit Convolution Network (R-GCN) is proposed to analyze transactions in a blockchain-based platform using the Stochastic Gradient Boosting (SGB) technique. The proposed framework is implemented in the Ethereum environment using Anaconda and Python packages. Also, an analogy of how these models can be applied in a range of smart technologies, such as the Unmanned Aerial Vehicle (UAV), Smart Grid, healthcare, and green infrastructure, is illustrated.
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-17942-x