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EnCTN: an enhanced AI-enabled deep learning framework for security enhancement in blockchain transactions.

P Bhuvaneshwari¹, A Krishnaveni², Y Harold Robinson³

¹School of Computer Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India. bhuvaneshwari.p@manipal.edu.

Scientific Reports

|November 27, 2025

Summary

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This summary is machine-generated.

This study introduces a deep learning-enabled blockchain framework for secure data management. The novel approach enhances data durability and anonymity, improving anomaly detection accuracy.

Area of Science:

Artificial Intelligence
Blockchain Technology
Data Security

Background:

Deep learning offers advanced solutions for Artificial Intelligence (AI)-based Blockchain frameworks.
Ensuring data reliability, confidentiality, and anonymity in blockchain transactions is crucial.
Existing methods require enhancement for robust data durability and propagation.

Purpose of the Study:

To propose a hybrid Blockchain and Deep Learning model for enhanced data durability and transaction analysis.
To develop a secure deep learning-enabled blockchain transaction model addressing confidentiality and anonymity.
To improve temporal anomaly detection in blockchain systems.

Main Methods:

Utilized an enhanced convolutional temporal network (EnCTN) for transaction analysis.

Keywords:

Anomaly detection Auto encoder Blockchain Deep learning Security Smart transactions Temporal convolution network

Related Experiment Videos

Employed a sliding window extraction technique for temporal series data.

Incorporated dilated convolution to capture long-range dependencies.

Implemented the framework in Ethereum using Python.

Main Results:

The proposed technique demonstrated improved performance over existing methods in several parameters.
Achieved enhanced anomaly classification accuracy on the NSL-KDD dataset.
The framework efficiently detects temporal anomalies with improved computational efficiency.

Conclusions:

The hybrid Blockchain and Deep Learning approach provides an efficient solution for real-world anomaly detection.
The EnCTN model significantly enhances data durability and propagation in blockchain systems.
The framework offers accurate discovery of temporal anomalies and improved computational efficiency.