Adaptive security is considered as an approach in cybersecurity that analyzes events and against events and behaviors to protect a network. This study will provide details about the different algorithms being used to secure networks. These approaches are driven by a small quantity of labeled data and a massive amount of unlabeled data. In this context, contemporary semi-supervised learning strategies base their operations on the assumption that the distributions of labeled and unlabeled data are comparable. This assumption has a substantial influence on how well these strategies perform overall. If unlabeled data contain information that does not belong to a particular category, the efficiency of the system will deteriorate.