A Unified Multi-Source Deep Learning Framework for Cybersecurity Anomaly Detection Using Cross-Modal Attention and Behavioral Representation
Traditionally, cybersecurity anomaly detection systems rely on only one source of data. However, these systems are no longer effective because modern cyber infrastructures are complex and versatile. This paper introduces a new technique called UMS, DLF: Unified Multi, Source Deep Learning Framework, which enables the fusion of multi, modal security data through cross, modal attention and behavioral representation learning. Initially, specialized feature extractors composed of convolutional layers for spatial flow patterns and temporal transformers for sequential dependencies are used to encode network flows, system log sequences, and user behavior profiles. Furthermore, a module for cross, modal attention fusion is proposed to dynamically assign weights to the inter, source correlations, thus preserving the essential contextual information across modalities. Continue reading to learn about how a behavioral embedding layer is used to capture the semantics of long, term user activities for robust anomaly characterization. Original paper evaluates the proposed method on cybersecurity benchmark datasets combined with the heterogeneous sources, such as UNSW, NB15 and CERT insider threat logs. Experimental results demonstrate that UMS-DLF achieves 97.84% detection accuracy, 96.91% precision, 97.36% recall, and an F1-score of 97.13%, outperforming conventional LSTM, CNN, and autoencoder baselines by 4.6–8.9% in F1 performance. Moreover, the model reduces false alarm rates to 2.1% and maintains real-time feasibility with an average inference latency of 18.4 ms per event. These results confirm that unified cross-modal learning significantly enhances anomaly detection reliability in complex cybersecurity environments.