Learning Robust Representations for Malicious Content Detection via Contrastive Sampling and Uncertainty Estimation

We propose Uncertainty Contrastive Framework (UCF), a Positive–Unlabeled (PU) representation learning approach that integrates uncertainty-aware contrastive loss, adaptive temperature scaling, and a self-attention–guided LSTM encoder to improve classification under noisy and imbalanced conditions. The method dynamically adjusts contrastive weighting based on sample confidence, stabilizing training using positive anchors, and adapts temperature parameters to batch-level variability. Applied to malicious content classifications, the resulting embeddings enabled multiple traditional classifiers to achieve over \(93.38\%\) accuracy, precision above 0.93, and near-perfect recall, with minimal false negatives and competitive ROC–AUC scores. Visual analyses confirmed distinct separation between positive and unlabeled instances, highlighting its ability to produce calibrated, discriminative embeddings. These results position UCF as a robust and scalable solution for PU learning in high-stakes domains such as cybersecurity and biomedical text mining.