Machine learning-based network intrusion detection systems can recognize complex traffic patterns, but often require substantial labelled attack data, degrade under deployment shift, and provide limited evidence for analysts. We present DeepWeave, a probabilistic neuro-symbolic Network Intrusion Detection System (NIDS) that makes logic an integral part of detection. DeepWeave automatically constructs and validates interpretable rules, represents neural predictions as probabilistic predicates, and reconciles both evidence sources through explicit coupling policies over packet-, flow-, and hybrid-views. The resulting proofs provide intrinsic explanations for rule-supported alerts. Across multiple datasets, DeepWeave improves low-data detection, false-positive control, transferability, and robustness. With only 50 labelled packet observations per attack class, it improves macro-F1 from 0.632 to 0.924 and reduces the attack false-positive rate from 0.075 to 0.008 over the matched neural baseline. Its proofs are also more stable than post-hoc neural attributions, while batched inference keeps probabilistic reasoning practical. These results demonstrate label-efficient, adaptable, and inspectable neuro-symbolic intrusion detection.

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