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Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial
Pukar Maharjan1, Kumar Shrestha1, Trilochan Bhatta1
1Advanced Sensor and Energy Research Laboratory, Department of Electronic Engineering, Kwangwoon University, Seoul, 01897, Republic of Korea.
Summary
This study introduces a novel hybrid nanogenerator using keystroke dynamics for biometric authentication. This AI-integrated system enhances security against cyberattacks by analyzing typing rhythms, achieving 99% accuracy.
Area of Science:
- Cybersecurity and Biometric Authentication
- Nanotechnology and Artificial Intelligence
Background:
- Password-based authentication systems are prevalent but vulnerable to various cyberattacks.
- Cyberattacks pose severe threats to personal information, finances, intellectual property, and national security.
Purpose of the Study:
- To develop a novel keystroke dynamics-based hybrid nanogenerator for biometric authentication and identification.
- To integrate artificial intelligence (AI) with the nanogenerator system to enhance security against password vulnerabilities.
Main Methods:
- Utilizing hybrid electromagnetic-triboelectric nanogenerators/sensors to convert keystroke mechanical energy into electrical signals.
- Feeding the generated electrical signals into an artificial neural network (ANN) based AI system for analysis.
- Employing keystroke dynamics to capture behavioral and contextual typing rhythm information for individual authorization.
Main Results:
- The self-powered hybrid sensors and neural network system achieved a high accuracy of 99% for biometric authentication.
- Demonstrated the system's capability to distinguish and authorize individuals based on their unique typing rhythms.
- Showcased the potential of the system as a robust security layer against common password vulnerabilities.
Conclusions:
- The developed keystroke dynamics-based hybrid nanogenerator integrated with AI offers a highly accurate and promising biometric authentication solution.
- This innovative approach provides a significant advancement in cybersecurity by offering a hybrid security layer to combat password vulnerabilities.
- The self-powered nature of the nanogenerator sensors enhances the practicality and efficiency of the biometric authentication system.
