Educational activities have also strongly promoted the use of artificial intelligence to improve efficiency. Online exams from Internet Service-Based Courses have become popular and may become a trend to prevent disease transmission and facilitate international students. However, from there, the question of how to detect cheating through online exams also arises. In this study, we collected videos from some Vietnamese colleges to deploy deep learning techniques for cheating detection in e-exams. We take advantage of the Mediapipe to extract skeletons from videos, and then, such skeletons are fetched into the model training phases. Through that, we also propose a Long Short-Term Memory Network (LSTM) for learning the extracted skeletons. In this way, the model contributed an astonishing accuracy of over 85% in detecting seven cheating behaviors in videos in e-exams.