README.md

metadata

language:
  - en
tags:
  - network-intrusion-detection
  - explainable-ai
  - cybersecurity
  - llm-generated

FlowExplain Training Dataset — Network Intrusion Explanations

711 examples pairing a classified network flow (from CICIDS2017) with a winning, natural-language explanation (label, reasoning, mitigation recommendation). Collected as part of FlowExplain, the system built for the diploma thesis "Explainable Network Intrusion Detection with Large Language Models" (Faculty of Computer and Information Science, University of Ljubljana).

• Code: mmalensek/SNIC_Network_Security
• Companion model (LoRA fine-tuned on this data): mmalensek/flowexplain-deepseek-r1-8b-lora

How the winning explanation was chosen

For each flow, three candidate explanations were generated in parallel:

1. a local model (deepseek-r1:8b via Ollama),
2. a cloud reference model (gpt-5.2),
3. the current fine-tuned local model (self-improving loop).

Candidates were scored by (1) deterministic checks (format compliance, label match, feature grounding), (2) an LLM-as-a-judge, and — for a subset — (3) pairwise comparison by human experts; the highest-scoring explanation per flow was kept as the training target.

Format

training_dataset.jsonl — one chat-format record per line:

{"messages": [
  {"role": "system", "content": "You are a network security expert..."},
  {"role": "user", "content": "Analyze this network traffic...\n\nCurrent flow:\n{...}"},
  {"role": "assistant", "content": "LABEL:\n...\n\nREASONING:\n...\n\nSOLUTION:\n..."}
]}

training_dataset.json contains the same examples with additional metadata (source file, predicted/actual label, raw flow features).

Source

Flow features are derived from CICIDS2017 (Sharafaldin, Lashkari & Ghorbani, 2018). Please also credit the original dataset when using this derived data.

Citation

If you use this dataset, please cite the thesis (mmalensek/SNIC_Network_Security).

Authors

Martin Malenšek — author. Mentor: izr. prof. dr. Veljko Pejović. Co-mentor: asist. Miha Grohar.