A Shoulder Surfing Resistant Graphical Authentication System (Android)

Abstract

This research focuses on developing a novel graphical authentication system robust against shoulder-surfing attacks. The system leverages a combination of randomized graphical puzzles and user-specific pattern recognition to enhance security compared to traditional password-based or simple image-based authentication. The methodology involves generating unique, complex graphical puzzles and evaluating user performance through pattern analysis. Results indicate a significant improvement in security against visual eavesdropping while maintaining user-friendliness. The system offers a practical solution to enhance security in public and insecure environments.

Introduction

Password-based authentication remains vulnerable to various attacks, including shoulder surfing – the act of visually observing a user's authentication process. This poses a significant security risk, especially in public spaces or when dealing with sensitive information. Existing graphical authentication methods often lack sufficient complexity or rely on easily predictable patterns. This research aims to address this gap by designing and implementing a shoulder-surfing resistant graphical authentication system that balances security with usability. The challenge lies in creating a system that is both difficult to compromise through visual observation and convenient for legitimate users.

Objectives

• Develop a graphical authentication system resistant to shoulder surfing attacks.
• Evaluate the usability and security of the proposed system through rigorous testing.
• Compare the performance of the proposed system with existing authentication methods.

Technical Details

• Randomized graphical puzzle generation and user-specific authentication paths
• Secure touch-based interaction with visual obfuscation layers
• Android Studio for application development
• SQLite for local secure user data storage
• Usability evaluation via user testing and logging