Abstract
Cheating attempts in educational assessments have long been observed. Because students today are characterized by their great digital intelligence, this negative conduct has intensified throughout the emergency remote teaching time. First, this article discusses the most innovative methods for combating cheating throughout the online evaluation procedure. Then, for this aim, a Convolutional Neural Networks for Cheating Detection System (CNNCDS) is presented.. The proposed solution has the advantage of not requiring the use of a camera, it recognizes and identifies IP addresses, records and analyzes exam sessions, and prevents internet browsing during exams. The K-Nearest Neighbor (K-NN) has been adopted as a classifier while the Principal Component Analysis (PCA) was used for exploratory data analysis and for making predictive models. The CNNCDS was learned, tested, and validated by using data extracted from a face-to-face exam session. its main output is a binary students' classification in real-time (normal or abnormal). The CNNCDS surpasses the fundamental classifiers Multi-class Logistic Regression (MLR), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naive Bayes (GNB) in terms of mean accuracy (98.5%). Furthermore, it accurately detected screen pictures in an acceptable processing time, with a sensitivity average of 99.8 percent and a precision average of 1.8 percent. This strategy has been shown to be successful in minimizing cheating in several colleges. This solution is useful for higher education institutions that operate entirely online and do not require the use of a webcam.