Artificial Intelligence-Driven Network Attack Traceability and Early Warning System

Abstract

In response to the increasing complexity of cyber attacks, traditional traceability technologies face bottlenecks in data processing and limitations in adaptability. This study proposes an artificial intelligence-driven network attack traceability and early warning system that constructs a closed-loop perception-decision-response architecture through the integration of multimodal AI technologies. At the theoretical level, it establishes a traceability framework integrating knowledge graphs with deep learning; at the algorithmic level, it employs temporal models and graph neural networks to reconstruct attack scenarios, combines multi-source data analysis to construct dynamic attacker profiles, and implements contextual risk assessment based on Bayesian networks; at the engineering level, it adopts a cloud-native microservices architecture and achieves module coordination through workflow engines. Tests demonstrate that the system excels in detection accuracy, warning timeliness, and resource efficiency, providing an innovative solution for building proactive security defense systems.