Temporal Threat Recognition in Supply Chains: Integrating Hidden Markov Models for Proactive Security with AI-Driven Automated Threat Hunting

In order to tackle the pressing problem of “Temporal Threat Recognition in Supply Chains,” this study presents a novel integrated model that combines RNNs with Hidden Markov Models (HMMs). Proactive security measures and operational integrity rely on the prompt and correct identification of temporal risks within the dynamic environment of supply chains. This is the crux of the issue statement. By combining RNN for fine-grained identification with HMM for high-level threat classification, our proposed system provides a holistic approach that can detect both broad patterns and subtle details. Our integrated model outperforms previous work in terms of performance measures, as seen by its 0.92 precision, 0.88 recall, 0.90 F1-Score, and 0.91 total accuracy. The model's usefulness in improving temporal threat identification is confirmed by its outperformance in precision, recall, accuracy, and AUC-ROC, as demonstrated in a comparative comparison with state-of-the-art approaches. The computational efficiency of the model makes it a viable option for realistic supply chain applications in the real world. In addition to bringing a fresh perspective to the field of temporal threat modelling, our study has practical implications for supply chain security and offers researchers and practitioners in the industry with a powerful and effective tool.