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Enhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning Cover

Enhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning

By: Junzhou Li,  Qianhui Sun and  Feixian Sun  
Open Access
|Aug 2023

Figures & Tables

dsj-22-1578-g1.png
Figure 1

Proposed Model.

Table 1

Model Assessment.

SENSITIVITY (TPR)SPECIFICITY (SPC)PRECISION (PPV)ACCURACY (ACC)F1 SCORE (F1)MATTHEWS CORRELATION COEFFICIENT (MCC)
0.98720.99270.99160.99010.98940.9802
Table 2

Result of Confusion matrix.

ACTUALPREDICTED
AttackNormal
Attack1162898
Normal15113317
dsj-22-1578-g2.png
Figure 2

ROC Curve.

Table 3

Comparative analysis.

REFERENCEMODELACCURACY (%)
K. Pradeep Mohan Kumar et al (2022)PPSF-BODL97.46
Alatawi, Mohammed Naif, et al (2023)PSO-GA followed by ELM-BA96.04
ProposedPrivacy-Preserving Secure Framework using LSTM-GRU99.01
Language: English
Submitted on: May 18, 2023
|
Accepted on: Jul 12, 2023
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Published on: Aug 31, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Junzhou Li, Qianhui Sun, Feixian Sun, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.