--- license: other license_name: cic-ids-2017-research-use license_link: https://www.unb.ca/cic/datasets/ids-2017.html pretty_name: CIC-IDS-2017 — Canonical NetFlow (flowprep) language: - en task_categories: - tabular-classification tags: - network-security - cybersecurity - netflow - flow - intrusion-detection - nids - canonical-schema - flowprep - deeptempo size_categories: - 100K ⚠️ This is a **small demonstration slice**, not the full CIC-IDS-2017 dataset. > For research, obtain the complete dataset from > [UNB CIC](https://www.unb.ca/cic/datasets/ids-2017.html) and cite accordingly > (see [Citation](#citation)). ## Why this exists Every network-ML project and SOC pipeline starts by solving the same unglamorous problem: flow data arrives with different column names, duration units, and timestamp encodings. The same field is `src_ip`, `Source IP`, `srcaddr`, or `ipv4_src_addr`; a duration might be seconds, ms, µs, or ns with nothing telling you which. flowprep maps 100+ column spellings onto one canonical schema and normalizes units, so labeled research data stays labeled and is ready to train on immediately. This dataset is that step, done once, published. ## How it was produced Fully reproducible from the sample bundled in the flowprep repo (no network access required): ```bash git clone https://github.com/DeepTempo/flowprep cd flowprep cargo build --release # aliased CIC columns (total_fwd_pkts, datetime timestamps) -> canonical schema ./target/release/flowprep canonicalize \ examples/cic2017_sample.parquet \ cic-ids-2017-canonical.parquet # -> Wrote 101094 flows ``` `flowprep` resolved the source's CIC-style aliases (e.g. `total_fwd_pkts` → `fwd_pkts`, `total_bwd_pkts` → `bwd_pkts`), converted the typed-datetime `timestamp` to int64 epoch microseconds, kept `flow_dur` in float64 seconds, and passed the ground-truth label columns through unchanged. The alias map is not hard-coded — it is loaded at compile time from flowprep's declarative [`schemas/netflow/v1/schema.json`](https://github.com/DeepTempo/flowprep/blob/main/schemas/netflow/v1/schema.json), the same artifact DeepTempo's production ingestion uses. Output is single-row-group ZSTD-compressed parquet. ## Schema | field | type | unit / encoding | description | |---|---|---|---| | `timestamp` | int64 | epoch **microseconds** | flow start time | | `src_ip` | string | | source IP | | `dest_ip` | string | | destination IP | | `src_port` | int32 | | source L4 port | | `dest_port` | int32 | | destination L4 port | | `fwd_bytes` | int64 | bytes | source→destination bytes | | `bwd_bytes` | int64 | bytes | destination→source bytes | | `fwd_pkts` | int64 | packets | source→destination packets | | `bwd_pkts` | int64 | packets | destination→source packets | | `flow_dur` | float64 | **seconds** | flow duration | | `protocol` | int32 | IANA number | **null in this slice** — the CIC sample carries no protocol field; kept for canonical-schema fidelity | | `attack` | string | `benign` / `attack` | ground-truth label (string) | | `label` | string | `0` / `1` | ground-truth label (binary, string-encoded) | `attack` and `label` are two encodings of the same binary ground truth and are preserved exactly as flowprep emitted them (label passthrough). This slice does **not** carry per-attack subtype labels (DoS, PortScan, …). ## Statistics - **Rows:** 101,094 flows - **Class balance:** 81,171 benign (80.29%) / 19,923 attack (19.71%) - **Unique source IPs:** 4,656 · **unique destination IPs:** 7,254 - **Timestamp range (as stored, passed through from source):** 2017-03-07 → 2017-07-07 UTC. CIC-IDS-2017 was captured over a work week in July 2017; the exact values here are the source sample's timestamps, unchanged by flowprep. - **`flow_dur`:** 0 – 120.0 s (mean 14.84 s) - **`fwd_bytes`:** 0 – 1,034,689 (mean 527) · **`bwd_bytes`:** 0 – 538,000,000 (mean 16,097) - **`fwd_pkts`:** 1 – 180,725 (mean 9.4) · **`bwd_pkts`:** 0 – 242,974 (mean 10.5) - **File:** `data/cic-ids-2017-canonical.parquet` (~1.7 MB, ZSTD, single row group) ## Usage ```python import pandas as pd df = pd.read_parquet("hf://datasets/DeepTempo/cic-ids-2017-flowprep/data/cic-ids-2017-canonical.parquet") # that's the whole integration — no bindings, no client library # binary label for training/eval y = (df["label"] == "1").astype(int) ``` ```python # or with the datasets library from datasets import load_dataset ds = load_dataset("DeepTempo/cic-ids-2017-flowprep", split="train") ``` ```python import duckdb duckdb.sql("SELECT attack, count(*) FROM 'data/cic-ids-2017-canonical.parquet' GROUP BY attack") ``` ## Intended use - A small, clean, **labeled** flow table for prototyping NIDS / flow-classification models and for **reproducible evaluation** (the canonical schema removes preprocessing variance — a documented source of up to ~40% metric swing across NIDS papers). - A worked example of canonical NetFlow for the new generation of **network foundation models**, which expect one stable, unit-normalized schema. - A reference for what flowprep output looks like before you point it at your own pcap / nfdump / OCSF / CSV sources. ## Limitations & honest notes - **Not the full dataset.** A ~101k-flow demonstration slice; not class-complete and not a substitute for the official CIC-IDS-2017 release. - **Binary labels only.** `attack`/`label` are benign-vs-attack; no per-attack subtype is present in this slice. - **`protocol` is null** (the source sample has no protocol field); the column is retained only for canonical-schema fidelity. - **Timestamps are passed through** from the source unchanged; treat them as relative ordering, not authoritative wall-clock capture times. - **Known CIC-IDS-2017 caveats apply.** The original dataset has documented labeling and generation issues discussed in the literature; do not treat results on this slice as production detection performance. ## License & attribution The **canonical parquet artifact and this dataset card** are released by DeepTempo; the **flowprep tool** that produced it is [Apache-2.0](https://github.com/DeepTempo/flowprep/blob/main/LICENSE). The underlying flow data **derives from CIC-IDS-2017**, © Canadian Institute for Cybersecurity (CIC), University of New Brunswick. It is redistributed here as a small conversion example, **with attribution and citation as required by CIC**. For research use, obtain the full dataset from [UNB CIC](https://www.unb.ca/cic/datasets/ids-2017.html) and cite the paper below. If you are a rights holder and have concerns about this slice, please open an issue on the [flowprep repo](https://github.com/DeepTempo/flowprep). ## Citation If you use this data, cite the original CIC-IDS-2017 paper: ```bibtex @inproceedings{sharafaldin2018toward, title = {Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization}, author = {Sharafaldin, Iman and Lashkari, Arash Habibi and Ghorbani, Ali A.}, booktitle = {Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP)}, year = {2018} } ``` And, optionally, the tool used to canonicalize it: ```bibtex @software{flowprep, title = {flowprep: network telemetry to ML-ready canonical NetFlow parquet}, author = {DeepTempo}, url = {https://github.com/DeepTempo/flowprep}, year = {2026} } ``` ## Acknowledgments - **Canonical Institute for Cybersecurity (UNB)** for CIC-IDS-2017. - Built with [**flowprep**](https://github.com/DeepTempo/flowprep) by [DeepTempo](https://deeptempo.ai).