GRAPHSECNET: A GRAPH NEURAL NETWORK FRAMEWORK FOR PREDICTIVE CYBERSECURITY INTELLIGENCE IN DYNAMIC NETWORK ENVIRONMENTS

Abstract

Contemporary cybersecurity threats exploit complex network topologies and temporal attack patterns that traditional detection systems fail to adequately model. This paper presents GraphSecNet, a novel Graph Neural Network framework that transforms network security data into dynamic graph representations for enhanced threat detection. The framework integrates Temporal Graph Attention Networks, self-supervised contrastive learning, and multi-perspective anomaly detection to capture both spatial network relationships and temporal attack evolution. Comprehensive evaluation on established cybersecurity datasets (CICIDS2017, UNSW-NB15, NSL-KDD) demonstrates significant performance improvements: 91.7% F1-score representing 9.8% improvement over state-of-the-art methods, 46% reduction in false positive rates, and scalable processing at 25,000 events per second. Graph attention mechanisms provide interpretable explanations for threat decisions, addressing critical gaps in explainable AI for cybersecurity. Statistical analysis confirms significance across all datasets (p < 0.001, Cohen's d > 1.8), validating the effectiveness of graph-based approaches for network threat intelligence.