Skip to content

Latest commit

 

History

History
118 lines (84 loc) · 4.04 KB

README.md

File metadata and controls

118 lines (84 loc) · 4.04 KB
Raw

Tavern-cut

Tavern Cut is an advanced cybersecurity application that integrates a Turing Test algorithm with artificial intelligence (AI) to enhance password protection and fortify online accounts against unauthorized access. This project aims to provide a robust and adaptive security mechanism by distinguishing between legitimate users and potential intruders through human-like conversational interactions.

Features

  • AI-Driven Turing Test: Utilizes AI to engage users in dynamic conversations during login attempts to assess authenticity.
  • Intruder Alert Mechanism: Sends automated email alerts to notify users of potential unauthorized access attempts.
  • Continuous Learning: The system evolves over time, improving its ability to distinguish between genuine users and threats through machine learning.
  • User-Friendly Interface: Provides a seamless and intuitive interface for user registration, login, and response to security alerts.

Installation

  1. Clone the repository:

    git clone https://github.com/saro0307/tavern-cut.git
cd tavern-cut

  2. Install required libraries:

    pip install -r requirements.txt

  3. Configure email settings: Replace the placeholders in the code with your actual email credentials:

    email_sender = '[email protected]'
email_password = 'your_password'
email_receiver = '[email protected]'

  4. Run the application:

    python tavern_cut.py

Usage

  1. User Registration:

    • Enter your email address and set up a secure password.

  2. Login:

    • Enter your username and password.
    • The system will engage in a human-like conversation to verify authenticity.

  3. Security Alerts:

    • In case of unauthorized access attempts, an email alert will be sent to the configured email address.

Code Overview

Turing Machine States and Transitions

The Turing machine is implemented to verify the password correctness through a series of states and transitions.

states = {
    "q0": {"r": ("q1", "r", "R")},
    "q1": {"u": ("q2", "u", "R")},
    "q2": {"s": ("q3", "s", "R")},
    "q3": {"s": ("accept", "s", "S")},
    "q4": {"i": ("accept", "i", "S")},
    "q5": {"a": ("accept", "a", "S")},
}

Email Alert Configuration

The send_email_alert function sends an email notification if an unauthorized login attempt is detected.

def send_email_alert():
    # Email configuration and sending logic

GUI Setup

The application uses Tkinter to create a user-friendly interface for login and registration.

root = tk.Tk()
root.title("Login Page")
# GUI components setup
root.mainloop()

Future Work

Future evaluations will focus on:

  • Scalability: Assessing system performance under increased user load.
  • Real-World Deployment: Testing the system in corporate networks and large-scale platforms.
  • Adaptability: Enhancing resilience against sophisticated AI-driven attacks.
  • User Experience: Continuously improving the user interface and reducing false positives.
  • Cross-Platform Compatibility: Ensuring seamless performance across various operating systems, browsers, and devices.
  • Legal Compliance: Ensuring adherence to privacy regulations and data protection laws.

Conclusion

Tavern Cut represents a significant advancement in cybersecurity, integrating the Turing Test algorithm with AI to provide a dynamic and adaptive defense mechanism. This project demonstrates the potential of AI in enhancing online security, offering a robust solution against evolving cyber threats.

Authors

  • Sam Priesly Mathuram P
  • Saravana Kumar G
  • Sudharsun B
  • Thiru Vigneswaran babu S

License

This project is licensed under the Apache-2.0 license. See the LICENSE file for details.

Acknowledgments

  • SNS College of Engineering, Coimbatore, Tamil Nadu, India
  • All contributors and supporters of the project

For more details, refer to the project report available here (Volume 10 Issue 5 – MAY 2024).