An Iterative Review Inspired Adaptive Cyber Threat Detection Model with Context Awareness & Meta-Learning with Generative Hybrid Anomaly Detection

¹ Research Scholar, Jhulelal Institute of Technology Nagpur, Maharashtra, India.
² Associate Professor, Jhulelal Institute of Technology Nagpur, Maharashtra, India.

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Al-driven cyber security frameworks must adapt to more sophisticated threats and context-based detections. We can improve interpretability, flexibility, and resilience in data-scarce and hostile settings in the current model. Challenges include model interpretation and explain ability, adaptability to fast-changing threat landscapes, and secure intelligence sharing among privacy-sensitive enterprises. This is done by creating a new framework using three cutting-edge methods: ACAML, GHAD, and FARL. Context-specific embeddings add meta-learning. Thus, the models will excel at threat adaptation to different contexts and outperform traditional models by 5-10% in accuracy and interpretability. To solve data shortage, GHAD creates high-quality synthetic data for balanced anomaly detection using diffusion models and autoencoders. An F1-score increase of 10% is expected. Federated adversarial reinforcement learning improves collaborative threat intelligence security and privacy and decentralised organization resilience by 30-40%. This method enhances cyber threat detection systems' adaptability, interpretability, and robustness. It offers scalable, real-time, resilient, and privacy-aware threat detection for critical infrastructure, healthcare, and finance. Finally, the proposed solutions will close cyber security gaps and enable proactive, safe, and context-aware AI-driven threat protection across operational landscapes.