AI and ML-Powered CAPTCHA and advanced graphical passwords: Integrating the DROP methodology, AES encryption and neural network-based authentication for enhanced security

Background Information: Advanced automated attacks and unauthorized access are frequently not prevented by traditional CAPTCHA and password procedures. Combining encryption, graphical passwords, AI, and ML provides a strong solution to today's cybersecurity issues, improving security and usability.
Objective: To create a thorough multi-layered authentication system that efficiently combats advanced cyberthreats by integrating AI-powered CAPTCHA, graphical passwords using the DROP approach, AES encryption, and neural network-based authentication.
Methods: The solution incorporates neural networks for behavioral analysis and real-time threat detection, graphical passwords based on DROP for dynamic engagement, AES encryption for safe data transport, and AI-driven CAPTCHA for human verification.
Results: The suggested approach outperforms conventional techniques in terms of speed, accuracy, and resistance to automated and brute-force attacks, achieving 96.8% accuracy, a false positive rate of 0.01%, and a security level of 9.5.
Conclusion The multi-layered strategy greatly improves authentication security, effectively thwarting sophisticated cyberthreats while maintaining a flawless user experience, which qualifies it for high-security settings.