HMSC-FuseMaskNet: A Hybrid Multi-source Neural Architecture for Context-Aware Data Security Classification

Accurate classification and grading of data based on sensitivity levels are crucial for ensuring information security and compliance. Traditional methods often struggle with heterogeneous data sources and evolving regulatory standards. To address these challenges, we propose HMSC-FuseMaskNet, a novel neural network architecture designed for context-aware data security classification. The model integrates three key components: (1) a multi-source encoder that processes both structured and unstructured data; (2) a cross-source semantic fusion module employing attention mechanisms to align data representations with relevant security standards; and (3) a learnable feature masking layer that emphasizes critical features while suppressing irrelevant ones, enhancing interpretability. We evaluate HMSC-FuseMaskNet on real-world datasets from finance, healthcare, and education sectors. The experimental setup includes comparisons with baseline models such as traditional machine learning classifiers and existing deep learning approaches. Results demonstrate that our model achieves superior performance, with an average accuracy improvement of 4.7% over baselines and enhanced interpretability as evidenced by feature attribution analyses. These findings suggest that HMSC-FuseMaskNet offers a flexible and effective solution for modern data security classification tasks.