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FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström

Ahmad Raza Khan1, Shaik Shakeel Ahamad1, Shailendra Mishra1

  • 1Department of Information Technology, College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia.

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Summary

A new Deep Learning (DL) model, FinSafeNet, enhances cybersecurity for digital banking transactions. It uses advanced AI techniques to detect threats with 97.8% accuracy, securing financial data effectively.

Keywords:
Cyber securityDeep learningDigital bankingFinSafeNetI-SLOAJoint mutual information maximisationMKPCA with Nyström approximation

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Area of Science:

  • Cybersecurity and Artificial Intelligence
  • Financial Technology (FinTech) Security
  • Machine Learning for Anomaly Detection

Background:

  • Increasing digital transactions elevate cybersecurity risks in banking.
  • Existing security models face challenges in real-time threat detection.
  • Need for robust solutions to protect financial data in online channels.

Purpose of the Study:

  • Introduce FinSafeNet, a novel Deep Learning model for securing digital banking transactions.
  • Address the technical challenges of real-time transaction security.
  • Improve the accuracy and efficiency of threat detection in financial data.

Main Methods:

  • Utilized a hybrid Deep Learning architecture: Bi-Directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and dual attention mechanism.
  • Employed Hierarchical Particle Swarm Optimization (HPSO) and Improved Snow-Lion Optimization Algorithm (I-SLOA) for feature selection.
  • Applied Multi-Kernel Principal Component Analysis (MKPCA) with Nyström Approximation for dimensionality reduction and feature analysis.
  • Incorporated advanced correlation measures and Joint Mutual Information Maximization for enhanced threat identification.

Main Results:

  • FinSafeNet achieved a high accuracy of 97.8% in detecting security threats on the Paysim database.
  • The model demonstrated effective feature selection and dimensionality reduction for large datasets.
  • Advanced correlation techniques improved the identification of relevant threat indicators.

Conclusions:

  • FinSafeNet offers a significant advancement in securing digital banking transactions against cyber threats.
  • The hybrid DL model provides efficient and accurate real-time threat detection.
  • The proposed methods enhance the robustness and performance of cybersecurity in FinTech.