KG-HiAttention: synergizing AI-based knowledge graphs and deep learning for explainable software vulnerability analysis
Pablo Chamoso
Software vulnerability analysis is critical for maintaining secure and reliable systems, yet traditional Deep Learning (DL) models often act as “black boxes,” lacking transparency and failing to leverage the explicit structural semantics of code. In this paper, we propose KG-HiAttention, a novel neuro-symbolic framework that synergizes sub-symbolic deep learning with symbolic AI-based Knowledge Graphs (KGs). We construct a CPG-inspired lightweight program graph for each software function, approx