The increasing reliance on third-party packages from repositories such as Python Package Index (PyPI) and Node Package Manager (NPM) has introduced critical vulnerabilities in software supply chains. Traditional security approaches, including signature-based detection and trust evaluation systems, demonstrate limited effectiveness against sophisticated supply chain attacks such as dependency confusion, typo squatting, and account takeover. The study employs dynamic analysis to capture runtime behaviors during package installation, constructing provenance graphs that represent system interactions as nodes (processes, files, network connections) and edges (operations). A specialized Graph Neural Network architecture processes these graphs to distinguish malicious patterns from legitimate package behaviors. Preliminary results demonstrate detection accuracy exceeding 92% with false positive rates below 8%, significantly outperforming existing static analysis tools. The system provides interpretable detection results through attention mechanisms that highlight suspicious behavioral patterns, enabling security teams to understand and respond to threats effectively.
In this work, we present a general Graph Neural Network (GNN) framework for learning algebraic properties of finite groups from their Cayley graph representations. The framework provides a unified computational pipeline consisting of a common graph construction procedure, feature…
The rapid growth of online career and learning resources has made it difficult for job seekers and professionals to identify the skills, roles, and learning paths that best match their goals. This paper presents SkillGraph, a multi-agent architecture for AIpowered career recommen…
Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, tra…
Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment" This repository contains the necessary codes to reproduce results in the paper: Baals, L. J., Liu, Y., Osterrieder, J., & Hadji-Misheva, B. (2025). A Syste…
We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability. Using undirected Cayley graph representations, the proposed framework learns to distinguish solvable and non-solvable groups directly from structural graph in…