GPML: Graph Processing for Machine Learning

TL;DR

GPML is a graph processing library that transforms network traffic data into graph representations to improve anomaly detection and forensic analysis in cybersecurity.

Contribution

It introduces a comprehensive graph-based framework for analyzing dynamic network behaviors, enabling advanced detection and forensic capabilities.

Findings

Supports real-time anomaly detection

Enables community and spectral metrics extraction

Enhances cybersecurity analysis

Abstract

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.