An Intelligent Framework for Automated Cyber Threat Data Synthesis and Network Log Anomaly Detection Using Deep Learning NLP Architectures

Modern enterprise environments generate vast volumes of unstructured log data daily across cloud and local network assets. Traditional Security Operations Centers (SOCs reliance heavily on rule-based signature detection systems, which consistently struggle with log volume scaling and fail against novel zero-day exploits, leading to catastrophic analyst alert fatigue. This paper presents an intelligent, automated framework that utilizes advanced Natural Language Processing (NLP) models to ingest unstructured network logs, extract critical security entities, and automatically map adversarial activities to the MITRE ATT&CK framework. By deploying a serverless ingestion pipeline combined with a fine-tuned Transformer-based classification engine, the proposed architecture filters ambient network noise and synthesizes complex threat payloads in real-time. Experimental evaluations demonstrate that the framework achieves a 94.2% precision rate in threat entity classification while dramatically reducing the manual threat parsing window from hours to fractions of a second, offering a scalable infrastructure defense mechanism for modern enterprise tech stacks.