Abstract

This proposal outlines the development of a Two-Way Graphical Password Authentication System using AI and machine learning techniques. The project aims to enhance the security and usability of authentication systems by incorporating graphical passwords. This system will require users to authenticate through both selection and drawing of graphical elements, thereby providing an additional layer of security. The integration with APIs using Flask will ensure seamless interaction between the authentication system and various applications.

Introduction

Traditional text-based passwords are increasingly vulnerable to various attacks, including phishing, brute force, and dictionary attacks. Graphical password schemes offer an innovative alternative, providing enhanced security and ease of use. This project proposes the development of a Two-Way Graphical Password Authentication System that combines the selection of images and drawing patterns for authentication. By leveraging machine learning algorithms, the system will ensure accurate and secure authentication processes.

Problem Statement

Text-based passwords are often weak and susceptible to security breaches. Users tend to create easily guessable passwords or reuse passwords across multiple platforms, leading to increased vulnerability. There is a need for a more secure and user-friendly authentication system that can protect against common attacks while being easy to remember and use.

Aim

The aim of this project is to develop a Two-Way Graphical Password Authentication System using AI and machine learning techniques to provide a secure and user-friendly alternative to traditional text-based passwords.

Objectives

1. Design Graphical Password Scheme Develop a robust graphical password scheme that includes both image selection and pattern drawing.

2. User Interface Development Create an intuitive and user-friendly interface for users to set and authenticate their graphical passwords.

3. Machine Learning Model Development Implement machine learning algorithms to recognize and verify graphical passwords accurately.

4. API Integration with Flask Develop and integrate APIs using Flask to connect the authentication system with various applications.

5. Security Enhancements Implement additional security measures to protect against common attacks such as shoulder surfing and screen recording.

6. Testing and Validation Conduct extensive testing to ensure the accuracy, security, and usability of the authentication system.

7. Deployment and Maintenance Deploy the authentication system on a scalable platform and establish a maintenance plan for ongoing improvements.

Research Methodology

1. Literature Review Conduct a comprehensive review of existing graphical password schemes and machine learning techniques in authentication to identify best practices and gaps.

2. Data Collection Gather and preprocess data for training machine learning models to recognize and verify graphical passwords.

3. Algorithm Selection Evaluate and select appropriate machine learning algorithms for graphical password recognition and verification.

4. Development Implement the authentication system using Python, integrating machine learning models, and develop APIs with Flask.

5. Testing Perform unit testing, integration testing, and user acceptance testing to validate the system’s performance.

6. Evaluation Analyze the system’s accuracy, security, and user satisfaction through feedback and performance metrics.

Conclusion

The proposed Two-Way Graphical Password Authentication System aims to provide a secure and user-friendly alternative to traditional text-based passwords. By leveraging AI and machine learning techniques, the system will offer enhanced security through graphical password schemes involving both image selection and pattern drawing. The successful implementation of this project will demonstrate the potential of graphical passwords in improving authentication security and usability.