AI-Driven Threat Intelligence using Graph Neural Networks for Advanced Cybersecurity Defense

The continuous growth of cyber threats needs sophisticated and adaptive defensive systems capable of proactively detecting malicious activity across complicated network architectures. This study presents TIGNET, a unique AI-driven threat detection architecture that combines sophisticated Graph Neural Networks (GNNs) with a bespoke feature improvement module called CAFE (Context-Aware Feature Extractor). TIGNET, which is designed to properly capture both structural and temporal relationships in network data, provides a multi-level reasoning framework for exact threat assessment. We test the model using the CSE-CIC-IDS2018 dataset, which comprises a wide range of attack scenarios spanning several days of simulated network activity. TIGNET outperforms baseline models such as SVM, Random Forest, LSTM, GCN, and GAT, with a superior accuracy of 98.12%, precision of 97.02%, recall of 97.89%, and an F1-score of 97.45%, thanks to thorough testing. The suggested model also has a low false positive rate (1.21%) and high detection rates for several attack types, including 99.45% for DoS Hulk and 98.91% for PortScan. The suggested architecture has a high potential for real-world application owing to its scalability, interpretability, and resilience. This study adds a realistic and new approach to the area of AI-powered cybersecurity by addressing both detection accuracy and dependability in dynamic threat environments.