A Hybrid Graph Neural Network Model for Predicting Cyber Attacks From Heterogeneous and Dynamic Network Data

Abstract

With valuable data constantly under attack, reactive security measures are no longer sufficient. Predicting cyber threats before they emerge is crucial. Cyberattacks do not occur randomly; they have a systematic underlying pattern. By discovering these patterns, it is possible to predict cyberattacks in advance. Unraveling the mysteries of these evolutionary patterns is quite challenging. Considering the potential of Graph Neural Networks to strengthen cybersecurity defenses, this paper proposes a new hybrid model, DyMHAG (Dynamic Meta-Path Heterogeneous Attention Graph). We propose a novel hybrid GNN model that integrates meta-path-based graph attention networks with the Gated Recurrent Unit (GRU) mechanism for temporal data processing. Our method consists of three layers: node-level attention-based graph embedding, meta-path-level attention-based graph embedding, and evolutionary pattern learning. This model aims to effectively capture complex relational structures and temporal dependencies in heterogeneous and temporally dynamic network data and provide a proactive solution for cyberthreat prediction. Preliminary evaluations indicate that our hybrid model not only improves prediction accuracy but also reduces the false positive rate, providing a more reliable defense against emerging cyber threats.