Integrating Multimodal Deep Learning for Real-Time Threat Detection A Cross-Domain Approach Combining Computer Vision, NLP, and Cybersecurity Analytics
https://doi.org/10.5281/zenodo.18871043
Keywords:
Multimodal Deep Learning, Threat Detection, Cybersecurity, Computer Vision, Natural Language Processing, System Logs, Real-Time Detection.Abstract
Background: The complexity of cyber threats means that it requires the coordination of multiple data types and types of attack surface to detect, contextualize, and respond to real-time threats, and single-modality detection systems fail to meet this need. Fusing multimodal data that include visual data of surveillance, textual information of communication and system logs denote a favored method of improving the threat detection function by means of cross-domain deep learning.
Objective: The objective of the research would be to craft and test a real-time threat detection system based on multimodal deep learning and incorporating computer vision, natural language processing (NLP), and cybersecurity analytics to enhance accuracy and a contextual comprehension of threats.
Method: The problem was addressed using the Gated Multimodal Transformer (GMT) architecture to combine three deep learning machines: a Vision Transformer (ViT) to work with surveillance videos, a BERT-based NLP model to detect threats in the text, and a Bi-LSTM to access system logs. Training was done by using public datasets and simulated attack settings. Its evaluation metrics consisted of precision, recall, F1-score, latency, and interpretation in the form of SHAP.
Results: Outcome multimodal fusion model obtained an F1- score of 90.9%, which is much higher than unimodal models in each type of threat, phishing, insider threats, unauthorized access, physical surveillance. The latency was less than 100 ms, which allows using it in real-time. SHAP analysis pointed out how decisions were easily interpretable and each modality had complementary features of the threat classification.
Conclusion: Multimodal deep learning is a strong, interpretable approach to real-time problem in solving modern cybersecurity issues via effective synthesis of cross-domain data. This strategy increases the accuracy of detection, responsiveness of operations in dynamic threat environments.
