Background: Most of the existing machine learning models for security tasks, such as spam detection, malware detection, or network intrusion detection, are built on supervised machine learning algorithms. In such a paradigm, models need a large amount of labeled data to learn the useful relationships between selected features and the target class. However, such labeled data can be scarce and expensive to acquire. Goal: To help security practitioners train useful security classification models when few labeled training data and many unlabeled training data are available. Method: We propose an adaptive framework called Dapper, which optimizes 1) semi-supervised learning algorithms to assign pseudo-labels to unlabeled data in a propagation paradigm and 2) the machine learning classifier (i.e., random forest). When the dataset class is highly imbalanced, Dapper then adaptively integrates and optimizes a data oversampling method called SMOTE. We use the novel Bayesian Optimization to search a large hyperparameter space of these tuning targets. Result: We evaluate Dapper with three security datasets, i.e., the Twitter spam dataset, the malware URLs dataset, and the CIC-IDS-2017 dataset. Experimental results indicate that we can use as low as 10% of original labeled data but achieve close or even better classification performance than using 100% labeled data in a supervised way. Conclusion: Based on those results, we would recommend using hyperparameter optimization with semi-supervised learning when dealing with shortages of labeled security data.