EBSSPA: Efficient Deep Learning Model for Enhancing Blockchain Scalability and Security Through Fusion Pattern Analysis

Objective: The article proposes a pattern analysis model to overcome scalability and security limitations in blockchain systems by applying advanced AI techniques.

Methods: To make the model scalable, the proposed model uses deep learning methods such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. Furthermore, random forest and convolutional neural networks (CNNs) are applied to augment security operations as an effective classier and anomaly detector on transaction data and a real-time threat detection on transaction patterns using the CNNs. By analysing time series data and dealing with long-term dependencies, the model uses RNNs and LSTMs to enable the strategic introduction of the model to predict and control network loads.

Results: When the proposed model is tested against a curated cloud dataset, it significantly outperforms the state-of-the-art approach in all the performance parameters. More specifically, it has exhibited a 5.05% increase in processing speed, 8.05% improvement in energy efficiency, and 5.27%, 5.8%, 10.24% and 11.62% better attack analysis precision, accuracy, recall and AUC, respectively.

Conclusion: The synergistic interaction of the applied AI techniques results in a blockchain paradigm that is both scalable and resilient to new security threats. This significant improvement in performance parameters demonstrates the effectiveness of integrating AI with blockchain technology to overcome scalability and security limitations, thereby enabling the widespread adoption of blockchain applications.