Adaptive Heuristic Rule-Infused Neural Architecture for Dynamic Cyber Threat Analysis and Packet Classification

The increasing complexity and volume of cyber threats, including phishing, DDoS, SQL injection, and zero-day exploits, challenge traditional intrusion detection systems that often rely on static signatures or shallow learning models. Existing techniques exhibit limitations in adaptability, explainability, and real-time decision-making under dynamic network environments. Static rule-based systems are equally limited, being unable to adapt to rapidly evolving attack vectors. To address these gaps, the study introduces a novel Synergistic Neuro-Heuristic Adaptive Cross-Attention Network (SNH-ACAN) integrated with a Heuristic Rule-Infusion Layer for enhanced interpretability and Meta-Reinforcement Learning (Meta-RL)–based Adaptive Feedback Module. The primary aim is to achieve explainable, high-performance cyber threat detection that fuses rule-based symbolic reasoning with neural feature learning. The framework employs Adaptive Rule Fusion (ARF) and the Dynamic Consistency Alignment Module (DCAM) to ensure alignment between heuristic and neural representations, minimizing misclassifications in complex packet sequences. To further enhance adaptability against zero-day and evolving threats, the Meta-Reinforcement Learning (Meta-RL) Adaptive Feedback Module is integrated post-classification. The model operates on preprocessed network traffic data from Kaggle, with data normalization, feature engineering, and heuristic rule embedding. Implemented using Python, TensorFlow, and PyTorch, the architecture employs cross-attention mechanisms to balance heuristic and learned information dynamically. Experimental findings indicate superior performance, achieving 98.2% accuracy, 98.5% precision, 98.0% recall, and 98.1% F1-score, and outperforming existing IDS models by 1.5–3%. The model also demonstrates enhanced scalability with reduced GPU memory usage and improved throughput (92%) under high network load. Overall, the proposed framework ensures enhanced cybersecurity resilience by dynamically responding to evolving cyber threats while minimizing detection delays and false positives.