GPML: Graph Processing for Machine Learning

2025/05/13 by Majed Jaber, Jaber, Majed, Julien Michel +5
Computer Science · #Semantic Web and Ontologies #Graph Theory and Algorithms #Advanced Graph Neural Networks

paper · pdf · doi:10.48550/arxiv.2505.08964

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.

Citations

• Towards the Development of Realistic Botnet Dataset in the Internet of\n Things for Network Forensic Analytics: Bot-IoT Dataset
• Defining and Evaluating Network Communities based on Ground-truth
• A Comprehensive Survey on Graph Neural Networks

Related

• GraphDART: Graph Distillation for Efficient Advanced Persistent Threat Detection
• PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security
• NetML: A Challenge for Network Traffic Analytics
• GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection
• XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model