The Technology
Behind Hypergraph

Discover how our proprietary Graph Neural Network architecture transforms network security through cutting-edge research and innovation.

What is a Graph Neural Network?

A Graph Neural Network (GNN) is a type of neural network designed to work directly with graph-structured data. Unlike traditional neural networks that process data in sequences or grids, GNNs can model the complex relationships and dependencies between interconnected entities.

In the context of network security, this means we can represent your entire infrastructure as a graph where devices are nodes and their communications are edges. This allows our AI to understand not just individual events, but the broader context of how different parts of your network interact.

Key Advantages of GNNs for Security

Model complex network topologies naturally
Capture multi-hop relationships between devices
Detect patterns across the entire network infrastructure
Understand context beyond isolated events
Scale efficiently with network size

Our Foundation Model Approach

Traditional cybersecurity AI solutions require months of fine-tuning for each specific network environment. This is time-consuming, expensive, and creates a vulnerability window during the training period.

Hypergraph GNN® takes a fundamentally different approach inspired by modern language models. We've pre-trained our model on massive datasets of network traffic, teaching it to understand the "language" of network communications at a fundamental level.

Why This Matters

Instant Deployment: No lengthy training period required - deploy in under 30 minutes
Zero-Shot Detection: Detect novel attacks without prior examples
Transfer Learning: Knowledge from one network helps protect others
Continuous Improvement: Model learns from the entire ecosystem
Cost Efficiency: Eliminate expensive per-client training processes

How It Works

Our technology combines spatial and temporal analysis to provide comprehensive threat detection

Data Ingestion

Network traffic is collected in real-time and transformed into graph representations where devices become nodes and communications become edges. This preserves the relational structure of your infrastructure.

Spatial Analysis

Our GNN analyzes the spatial structure of your network, understanding device relationships and normal communication patterns. This context-aware approach identifies anomalies that isolated analysis would miss.

Temporal Aggregation

Multiple temporal snapshots are stacked and analyzed together to track how patterns evolve over time. This enables detection of sophisticated multi-stage attacks that unfold gradually.

Threat Detection

Anomalies are identified through both spatial and temporal analysis, with contextual understanding reducing false positives by 87% while maintaining a 97.9% recall rate for real threats.

Our Research

Hypergraph's technology is built on rigorous academic research published at top-tier conferences

"PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security"

10th IEEE European Symposium on Security and Privacy2025

We present PPT-GNN, a practical pre-trained spatio-temporal graph neural network for network security. Our model leverages both spatial and temporal information to detect sophisticated attack patterns that traditional methods miss. Through extensive evaluation on real-world datasets, we demonstrate significant improvements in detection accuracy while maintaining low false positive rates.

View Paper Cite

"Towards a graph-based foundation model for network traffic analysis"

Graph Neural Networking Workshop - GNNet2024

This work explores the development of a graph-based foundation model specifically designed for network traffic analysis. By treating network communications as graph structures, we enable the model to capture complex relationships and patterns that are invisible to traditional sequential analysis methods. Our approach demonstrates the potential for transfer learning in the cybersecurity domain.