DeepWeave: Weaving Neural and Symbolic Evidence for ML-Based Network Intrusion Detection System

DeepWeave is a neuro‑symbolic network intrusion detection system that integrates logic rules with neural predictions to provide interpretable, probabilistic evidence for alerts. It automatically generates and validates rules, couples evidence across packet, flow, and hybrid views, and produces proofs that explain rule‑supported detections. In experiments, DeepWeave achieves higher macro‑F1 scores and lower false‑positive rates than a purely neural baseline, even with only 50 labeled packets per attack class, and its explanations are more stable than post‑hoc neural attributions.

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