arXiv:2610.03999v1 Announce Type: new 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.
SERA-IDS: Structured Experience Retrieval-Augmented Intrusion Detection with Small Language Models
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