| --- |
| 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](https://github.com/mmalensek/SNIC_Network_Security) |
| - **Companion model (LoRA fine-tuned on this data):** [mmalensek/flowexplain-deepseek-r1-8b-lora](https://huggingface.co/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: |
|
|
| ```json |
| {"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](https://www.unb.ca/cic/datasets/ids-2017.html) |
| (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](https://github.com/mmalensek/SNIC_Network_Security)). |
|
|
| ## Authors |
|
|
| Martin Malenšek — author. Mentor: izr. prof. dr. Veljko Pejović. Co-mentor: asist. Miha Grohar. |
|
|
