Hyperparameter Optimization in Neuro-symbolic Unsupervised Graph Learning

Hyperparameter Optimization in Neuro-symbolic Unsupervised Graph Learning

연구 분야: Artificial Intelligence

학회: International Conference on Availability, Reliability and Security

Detecting network intrusions without labeled data remains challenging due to severe class imbalance, evolving traffic patterns, and computational complexity in realistic scenarios. To address these issues, we propose a fully unsupervised neuro-symbolic graph-learning pipeline that integrates symbolic reasoning into graph neural representations, enhancing interpretability and robustness. Our key contributions include a novel feature-selection strategy driven by unsupervised community graph detection, a memory-efficient line graph construction reduced via minimum-spanning trees, and a lightweight symbolic layer providing human-readable explanations of anomalies. Evaluations on IoTID20 and UNSW-NB15 benchmarks yield Matthews Correlation Coefficient (MCC) scores of 0.97 and 0.91, significantly surpassing recent unsupervised graph-based baselines demosntrating the practical viability and effectiveness of neuro-symbolic frameworks as an innovative and interpretable approach for unsupervised graph network intrusion detection.