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An efficient multi-factor authentication scheme based CNNs for securing ATMs over cognitive-IoT.

Ahmed Shalaby¹, Ramadan Gad¹, Ezz El-Din Hemdan¹

¹Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menouf, Menoufia, Egypt.

Peerj. Computer Science

|April 5, 2021

Summary

This summary is machine-generated.

This study introduces a secure ATM authentication system using iris recognition via mobile apps. It enhances security against traditional ATM fraud and performs well even with noisy iris images.

Keywords:

ATM Chaotic encryption Cognitive IoT Convolutional neural networks Deep learning Iris recognition Mobile application

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Area of Science:

Biometrics and Security Engineering
Artificial Intelligence in Finance
Internet of Things (IoT) Security

Background:

Traditional ATM security (card and PIN) faces significant vulnerabilities like theft and loss.
Iris biometrics offer a robust alternative for identity verification in secure environments.
Integrating advanced biometrics into IoT applications like ATMs presents unique challenges.

Purpose of the Study:

To propose a novel, efficient, and secure full authentication system for ATMs.
To enhance bank client verification using iris recognition integrated with mobile applications.
To address security concerns in Cognitive Internet of Things (C-IoT) environments.

Main Methods:

Utilizing a deep Convolutional Neural Network (CNN) for iris feature extraction.
Employing a fully connected neural network (FCNN) with a Softmax layer for classification.
Implementing chaotic encryption for secure iris template transmission over the internet.
Evaluating performance under various noisy iris image conditions.

Main Results:

Achieved highly competitive recognition accuracy rates.
Demonstrated a low degradation in accuracy with noisy iris images.
Exhibited a relatively low training time, crucial for real-time applications.
Showcased the effectiveness of CNN and FCNN in iris recognition.

Conclusions:

The proposed system offers an efficient and secure solution for ATM authentication.
Iris recognition, combined with mobile app integration and CNN/FCNN, significantly improves ATM security.
The system's resilience to noisy data and fast training time make it suitable for critical banking IoT applications.