In many real-world applications, abnormal behaviors must be detected immediately to avoid dangerous situations. Several automated approaches have been proposed that aim to analyze the data collection provided and identify critical and dangerous patterns. This task was always considered as unsupervised learning since no labeled instances were available: obtaining training labels is extremely expensive and requires a lot of time from experts who have to carefully read the data and provide the labels. However, nowadays there are often few labels available, so many semi-supervised models have been studied, which significantly improve the unsupervised performance. Semi-supervised models are divided into three categories depending on their approach.