  This study addresses the current issues in online assessments, which are
particularly relevant during the Covid-19 pandemic. Our focus is on academic
dishonesty associated with online assessments. We investigated the prevalence
of potential e-cheating using a case study and propose preventive measures that
could be implemented. We have utilised an e-cheating intelligence agent as a
mechanism for detecting the practices of online cheating, which is composed of
two major modules: the internet protocol (IP) detector and the behaviour
detector. The intelligence agent monitors the behaviour of the students and has
the ability to prevent and detect any malicious practices. It can be used to
assign randomised multiple-choice questions in a course examination and be
integrated with online learning programs to monitor the behaviour of the
students. The proposed method was tested on various data sets confirming its
effectiveness. The results revealed accuracies of 68% for the deep neural
network (DNN); 92% for the long-short term memory (LSTM); 95% for the
DenseLSTM; and, 86% for the recurrent neural network (RNN).
