GPML: Graph Processing for Machine Learning
This provides a graph-based tool for cybersecurity professionals to enhance real-time threat detection and forensic analysis in dynamic networks.
The authors tackled the problem of detecting complex cyber-attacks in dynamic networks by developing the GPML library, which transforms network traffic into graph representations to enable anomaly detection and community shift analysis.
The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML (Graph Processing for Machine Learning) library addresses this need by transforming raw network traffic traces into graph representations, enabling advanced insights into network behaviors. The library provides tools to detect anomalies in interaction and community shifts in dynamic networks. GPML supports community and spectral metrics extraction, enhancing both real-time detection and historical forensics analysis. This library supports modern cybersecurity challenges with a robust, graph-based approach.