Comprehensive Security Monitoring System for Online Examinations Using Keylogger, Emotion Detection, Head Movement Analysis and Gadget Identification

In the digital age online tests have become the norm for evaluation yet, their credibility is undermined by their great susceptibility to cheating, illegal answer communication and the use of other technological devices. Sophisticated cheating strategies including subtle head motions for secret communication, emotional indications that indicate stress or dishonesty and unapproved gadget use are not adequately monitored or detected by traditional proctoring approaches. This study suggests an AI-powered real-time surveillance system that incorporates several cutting-edge methods to guarantee exam integrity in order to address these issues. In order to detect indications of answer sharing a continuous keylogger-based tracking mechanism logs keyboard inputs and uses Natural Language Processing (NLP) techniques such as Named Entity Recognition (NER) and text classification. Concurrently a facial behavior analysis module based on a Convolutional Neural Network (CNN) tracks the examinee's movements of the head (97% accuracy) and feelings (99.3% accuracy) to look for possible signs of cheating. Furthermore, the existence of cell phones, computers or other prohibited devices in the testing setting is detected by a YOLOv8-based electronic device detection model.