arXiv:2610.03999v2 Announce Type: replace Abstract: Large language models (LLMs) offer a flexible approach to network intrusion detection, but direct classification of numerical flow records can be unreliable without traffic- specific decision boundaries. Retrieval-augmented generation (RAG) enables LLMs to leverage external experience; however, free-form textual experience requires the model to infer the numerical conditions that distinguish traffic classes. We propose SERA-IDS, a Structured Experience Retrieval-Augmented Intru- sion Detection framework that learns decision knowledge from classification errors. A Small Language Model (SLM) analyzes misclassified flows and generates structured rules containing behavioral descriptions, feature conditions, class-confusion infor- mation, tool-derived confidence, and provenance. A confidence gate admits supported rules into the Experience Library. During testing, the library is frozen, and retrieved class-diverse rules are condition-matched to the query flow and provided as evidence to the decision SLM. The same library is evaluated with three small, locally deployable SLMs, namely Llama 3.1, Phi-4:14B, and Qwen2.5:7B, without fine-tuning or paid hosted inference. On the test sets, SERA-IDS improves macro F1 on NF-BoT- IoT from 11.66%, 9.26%, and 7.83% to 86.46%, 69.50%, and 83.51%, respectively, and on NF-ToN-IoT from 3.82%, 6.17%, and 1.94% to 84.16%, 87.66%, and 83.37%. These results show that structured experience retrieval enables small, free, and locally deployable SLMs to achieve strong intrusion-detection performance, including performance exceeding that of the re- ported existing work used in our comparison.