Cyber Sentinel: AI-Powered Anomalous Behavior Detection System

Abstract

User authentication has been and still remains the most exploited of all weak points in the digital worlds of today. Cybersecurity incidents. The project named "CyberSentinel: AI-Powered Anomalous Behavioral Detection System" introduces a smart authentication method that consists of three factors working together to make system access more secure by the simultaneous application of password checking, face recognition, and keystroke dynamics reporting. CyberSentinel, as opposed to traditional login systems that either depend entirely on passwords or on the use of a single biometric trait, integrates the use of artificial intelligence in a way that it has three levels of verification. The first level checks the user’s identity by comparing the password. The second level applies a face-examination model that obtains a still image at the time of login to check the rightful user through deepface and spoof detection to block facial impersonation. The third level depends on biometrics of behavior by exploring users’ keystroke patterns like typing speed, holding time, and latency with a Long Short-Term Memory (LSTM) neural network. The system was developed in Python and made possible by the use of the most advanced frameworks including TensorFlow, OpenCV, and Scikit-learn. The backend is supported by FastAPI (Fast Application Programming Interface) and is connected to a ReactJS (React JavaScript Library) based user interface. The system will use a fusion-based decision mechanism which means it will check the results coming from all three modules and allow access only when there is an agreement among them. This proposed structure is good enough to detect strange login attempts, impersonations, and wrongful access, and still be user-friendly. Merging physiological and deepface for emotion detection along with AI-powered analytics, CyberSentinel is a secure, adaptive, and efficient authentication solution that lessens the impact of the inherent drawbacks of single-factor systems.