Cybersecurity
GNNs have emerged as a powerful tool for cybersecurity applications, leveraging the inherent graph structure of many security-related datasets to detect threats and anomalies.
Why graphs for cybersecurity?
Many cybersecurity datasets and problems naturally lend themselves to graph representations:
- Network traffic and communications can be modeled as graphs, with devices as nodes and connections as edges.
- System call traces form temporal graphs of process interactions.
- Social networks used for fraud detection are inherently graph-structured.
- Software dependency graphs represent relationships between code components.
By using graph-based models, we can capture and analyze complex relationships and patterns that may be missed by traditional machine learning (ML) approaches.
Network intrusion detection
GNNs have shown promise for detecting network intrusions and anomalies by analyzing traffic patterns. In this application, the following...
