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Graph-Based Cybersecurity Risk Assessment Algorithm Link to heading

Keywords: Cybersecurity · Knowledge Graphs · Ontology · Synthetic Data Generation

Context & Highlights

• (2023-2025) 2-year Data Science apprenticeship at Orange Innovation | DATA-AI Entity
• Co-authored research paper on constraint-based Knowledge Graph generation (under review)
• Evolved PyGraft into PyGraft-gen with ontology extraction and large-scale optimization
• Built conference demonstrator for algorithmic risk assessment solution

Overview: As real-world network data for cybersecurity research is often too sensitive to share, it becomes critical to offer solutions for realistic yet synthetic graph generation. I conducted state-of-the-art analysis on existing tools and evolved PyGraft, an open-source synthetic schema and KG generator, into PyGraft-gen with two major advances: real-world ontology extraction and optimized large-scale generation. This open-source tool enables researchers to use any network or cybersecurity ontology to generate realistic Knowledge Graphs at scale for testing diverse solutions. Additionally, I developed a demonstrator application for conferences to show why manual optimization is impractical and our algorithmic solution necessary.