LLM-Based Optimized Adaptive Threat Monitoring Framework for Malicious Domain and Adversarial URL Detection Process

Abstract

The malicious domains and adversarially crafted URLs in cyber threats evolve at a very high speed. The detection frameworks need to be robust, adaptive, and scalable in such scenarios. Traditional detection mechanisms are static feature-based approaches that cannot perform well against unseen threats, adversarial manipulations, and long-term attack evolutions. Existing systems lack granular threat attribution, cross-organization intelligence sharing, and adversarial robustness, making them unsuitable for modern cyber defenses. To address these limitations, we introduce an LLM-Based Frequent Monitoring Framework that combines five advanced techniques: Meta-Learned Self-Supervised Domain Generalization (ML-SSDG), Reinforcement Learning-Augmented Adversarial Training (RL-AdvTrain), Hierarchical Multi-Task Threat Classification (HMT-TC), Temporal Memory-Augmented Transformer for Sequential Threat Detection (TMAT-STD), and Federated Privacy-Preserving Threat Intelligence Learning (FPPTIL). ML-SSDG can achieve zero-shot detection for novel attack domains with a reduction of false negatives by 20% and enhancement of zero-shot accuracy by up to 30%. RL-AdvTrain strengthens the model against masked malicious URLs, detecting 40% more adversarial threats. HMT-TC improves threat attribution and increases classification accuracy for attacks by 50%. TMAT-STD allows for identifying emerging domain threats in real-time, while such detection reduces the response time to domain-based malware campaigns by 30%. The last one is FPPTIL, which allows shared cross-organization threat intelligence without sharing the source of private data. The process of global threat detection improves by 30%. Our framework achieves a holistic, real-time, and privacy-preserving cyber defense solution that adequately outperforms traditional approaches in adversarial resilience, threat attribution, and zero-shot detections. Taken together, these improve the cybersecurity posture, reduce false positives, and support proactive mitigation of emerging cyber threats at scale in process.