MIND-SHIELD: A Multi-Intelligence Deep Learning Framework for Hierarchical Threat Detection and Zero-shot Situational Intelligence Process

With respect to increasing complexity in the emerging and frequent threats in national security and cyber defense architectures as well as critical infrastructure monitoring, there arises a need for generalizable threat detection systems, which are robust and real time in nature, currently built predominantly on siloed modalities and static feature extraction along with supervised training paradigms. Some limitations do persist, especially in the capabilities of covert or evolving threat detection, such as in low-data or zero-shot cases. The systems indeed are further incapable of integrating heterogeneous data sources like surveillance feeds, geolocation traces, and social intelligence into a temporally coherent whole. To close the said gaps, we introduce MultiIntelligence Neural Detection System for Hierarchical Integrated Evaluation of Latent Dangers (MIND-SHIELD) as an end-to-end multi-module deep learning framework built specifically for spatio-temporal threat intelligence. 364 The pipeline begins with CAST-Net, a context-aware spatio-temporal transformer network that generates joint embeddings from multi-modal signals through hierarchical cross-modal attention. The embeddings are then passed to an adversarial latent reasoning graphs (ALRG), a Bayesian adversarial graph neural network that models covert actor-intent relations. Multi-Modal Contrastive Anomaly Mining Network (MCAM-Net) uses adaptive contrastive learning to isolate anomalies that are not threat-specific. The output would then be further enhanced by Semantic Memory-Augmented Neural Intelligence System (SMNIS) whereby dynamic attention over historical threat vectors would contribute to augmenting context understanding. The final product is an interfusion of all these representations under ZS-SIM (Zero-shot Situational Intelligence Model), which simulates probable threat trajectories under zeroshot conditions, using a transformer-based decoder. Empirical evaluations bring forth impressive improvements of +18.3% in early threat detection, +23.6% in trajectory prediction, and +22.6 in anomaly F1-score over the norms set by these methods. MIND-SHIELD thus enables anticipatory intelligence, cross-domain generalization, operational readiness for deployment, and emerging technology in AI-based threat detection process.