Cross-Modal Deep Learning for Real-Time Threat Detection Using CV, NLP, and Cyber Analytics

The increasing sophistication of cyber threats within modern digital ecosystems has exposed significant limitations in conventional silo-based cybersecurity systems. Traditional unimodal threat detection mechanisms often fail to correlate heterogeneous data sources such as surveillance imagery, phishing communications, and behavioral logs, leading to delayed response times, elevated false-positive rates, and reduced contextual awareness. To address these limitations, this study proposes a multimodal deep learning framework integrating computer vision, natural language processing (NLP), and structured cybersecurity analytics for enhanced real-time threat detection. The proposed architecture combines a Vision Transformer (ViT) for visual anomaly recognition, a BERT-based transformer for textual threat classification, and a Bi-LSTM network for behavioral log analysis. Outputs from individual modalities are fused using a Gated Multimodal Transformer (GMT) with cross-modal attention mechanisms to improve contextual understanding and threat classification accuracy. Experimental evaluation was conducted using benchmark datasets including UCF-Crime, VIRAT, phishing email corpora, and structured SIEM-generated logs. The multimodal fusion model achieved 92.3% precision, 89.7% recall, 90.9% F1-score, and 91.5% accuracy, significantly outperforming unimodal baseline models. SHAP-based explainability further enhanced model transparency by identifying influential visual, textual, and behavioral threat indicators. The findings demonstrate that multimodal deep learning architectures provide scalable, interpretable, and context-aware solutions for next-generation intelligent cybersecurity systems.