Datasets:

DeepTempo
/

cic-ids-2017-flowprep

timestamp int64 1,488,848B 1,499,432B	src_ip stringlengths 7 15	dest_ip stringlengths 7 15	src_port int32 0 65.5k	dest_port int32 0 65.5k	fwd_bytes int64 0 1.03M	bwd_bytes int64 0 538M	fwd_pkts int64 1 181k	bwd_pkts int64 0 243k	flow_dur float64 0 120	protocol int32	attack stringclasses 2 values
1,488,876,958,000,000	8.254.250.126	192.168.10.5	80	49,188	12	0	2	0	0.000004	null	benign
1,488,877,019,000,000	192.168.10.9	192.168.10.3	1,056	88	2,812	2,820	7	4	0.000655	null	benign
1,488,877,062,000,000	192.168.10.17	192.168.10.3	35,504	88	3,150	3,152	10	6	0.001122	null	benign
1,488,877,116,000,000	23.217.36.24	192.168.10.14	443	49,480	12	0	2	0	0.000001	null	benign
1,488,877,119,000,000	23.50.69.163	192.168.10.14	80	49,488	12	0	2	0	0.000001	null	benign
1,488,877,048,000,000	192.168.10.12	96.126.105.86	123	123	432	432	9	9	72.994028	null	benign
1,488,877,078,000,000	192.168.10.50	69.89.207.99	123	123	384	384	8	8	69.018971	null	benign
1,488,877,144,000,000	192.168.10.9	192.168.10.3	123	123	192	192	4	4	64.022097	null	benign
1,488,877,314,000,000	192.168.10.9	192.168.10.50	1,075	22	6	6	1	1	0.00015	null	benign
1,488,877,332,000,000	192.168.10.9	172.217.11.14	1,100	80	12	0	3	1	5.151578	null	benign
1,488,877,355,000,000	192.168.10.19	192.168.10.3	55,906	88	3,150	3,152	10	6	0.001128	null	benign
1,488,877,386,000,000	192.168.10.14	192.168.10.3	49,422	88	2,780	2,820	7	4	0.00051	null	benign
1,488,877,404,000,000	192.168.10.14	192.168.10.3	49,440	88	486	444	7	4	0.00057	null	benign
1,488,877,046,000,000	192.168.10.12	192.168.10.255	8,612	8,610	828	0	46	0	106.398967	null	benign
1,488,877,449,000,000	192.168.10.14	2.23.106.163	49,469	80	1,594	214	4	5	0.21741	null	benign
1,488,877,457,000,000	192.168.10.14	192.168.10.3	49,474	88	470	370	7	4	0.000547	null	benign
1,488,877,472,000,000	192.168.10.16	192.168.10.3	33,608	389	165	326	10	4	0.000518	null	benign
1,488,877,484,000,000	192.168.10.9	54.148.59.150	1,112	443	37	0	2	0	0.000262	null	benign
1,488,877,458,000,000	192.168.10.14	192.168.10.3	49,477	49,666	5,556	2,566	19	16	40.029515	null	benign
1,488,877,431,000,000	192.168.10.14	23.49.133.163	49,465	80	145	671	5	3	78.244537	null	benign
1,488,877,518,000,000	216.58.217.68	192.168.10.9	443	1,103	12	0	2	0	0.000215	null	benign
1,488,877,553,000,000	192.168.10.9	52.84.145.142	1,149	443	390	3,716	8	6	5.082551	null	benign
1,488,877,559,000,000	192.168.10.9	172.217.10.46	1,117	443	135	46	5	1	5.995163	null	benign
1,488,877,324,000,000	192.168.10.50	224.0.0.251	5,353	5,353	540	0	12	0	0.000016	null	benign
1,488,877,589,000,000	192.168.10.14	72.167.239.239	49,568	80	12	0	2	0	0.000257	null	benign
1,488,877,586,000,000	192.168.10.14	52.84.145.26	49,518	443	390	3,716	8	6	5.427776	null	benign
1,488,877,484,000,000	192.168.10.14	23.63.226.169	49,490	80	300	247	5	3	109.969709	null	benign
1,488,877,597,000,000	192.168.10.14	178.255.83.1	49,651	80	12	0	2	0	0.00008	null	benign
1,488,877,601,000,000	192.168.10.14	72.5.205.41	49,658	443	43	0	3	0	0.003192	null	benign
1,488,877,601,000,000	192.168.10.14	72.30.202.150	49,693	443	602	6,568	12	9	0.384452	null	benign
1,488,877,603,000,000	192.168.10.16	173.194.66.108	43,580	465	37	0	2	0	0.000018	null	benign
1,488,877,602,000,000	192.168.10.14	63.251.98.12	49,721	443	950	6,686	9	10	1.319334	null	benign
1,488,877,588,000,000	192.168.10.14	198.22.162.9	49,555	443	1,243	9,723	12	13	16.071999	null	benign
1,488,877,602,000,000	192.168.10.14	23.194.141.179	49,735	443	382	3,938	8	6	5.482442	null	benign
1,488,877,502,000,000	192.168.10.14	23.194.182.11	49,494	443	977	6,515	14	9	109.146092	null	benign
1,488,877,476,000,000	192.168.10.16	99.224.25.39	123	123	384	384	8	8	69.975674	null	benign
1,488,877,615,000,000	192.168.10.14	208.81.233.48	49,840	443	12	0	2	0	0.006766	null	benign
1,488,877,612,000,000	192.168.10.14	198.8.70.212	49,798	443	608	2,719	8	5	5.150803	null	benign
1,488,877,618,000,000	172.217.11.2	192.168.10.16	443	54,522	0	0	3	0	0.007602	null	benign
1,488,877,619,000,000	172.217.9.228	192.168.10.16	443	58,562	0	0	2	0	0.016047	null	benign
1,488,877,619,000,000	34.204.162.61	192.168.10.14	443	49,819	43	0	3	0	0.000004	null	benign
1,488,877,621,000,000	192.168.10.16	108.174.11.1	37,062	443	559	3,125	10	7	0.149526	null	benign
1,488,877,618,000,000	192.168.10.16	72.21.91.29	48,090	80	0	0	3	1	5.648256	null	benign
1,488,877,494,000,000	192.168.10.17	208.79.89.249	123	123	96	48	2	1	66.000104	null	benign
1,488,877,627,000,000	192.168.10.12	172.217.11.2	45,474	443	2,082	10,233	15	16	0.259353	null	benign
1,488,877,628,000,000	192.82.242.50	192.168.10.12	443	54,662	43	6	2	1	0.002231	null	benign
1,488,877,616,000,000	192.168.10.14	199.233.57.19	49,849	443	6,006	8,872	13	16	16.266867	null	benign
1,488,877,394,000,000	192.168.10.17	192.168.10.255	138	138	2,820	0	12	0	0.00002	null	benign
1,488,877,641,000,000	192.168.10.9	208.185.118.98	1,191	443	372	3,939	7	8	0.128057	null	benign
1,488,877,519,000,000	192.168.10.19	159.203.158.197	123	123	96	96	2	2	67.023862	null	benign
1,488,877,605,000,000	192.168.10.16	23.50.75.27	54,684	80	864	3,546	12	11	59.001552	null	benign
1,488,877,664,000,000	192.168.10.9	52.40.24.51	1,194	443	404	3,113	9	6	6.018095	null	benign
1,488,877,617,000,000	192.168.10.16	65.39.197.206	43,388	80	391	349	10	10	65.049031	null	benign
1,488,877,691,000,000	192.168.10.5	192.168.10.3	49,168	88	654	3,064	9	4	0.00089	null	benign
1,488,877,703,000,000	192.168.10.14	23.50.75.27	49,522	80	12	0	2	0	0.000054	null	benign
1,488,877,707,000,000	54.192.36.9	192.168.10.14	443	49,603	12	0	2	0	0.00023	null	benign
1,488,877,710,000,000	192.168.10.5	192.168.10.3	49,194	389	4,982	1,566	15	12	0.431586	null	benign
1,488,877,592,000,000	192.168.10.14	152.163.66.131	49,607	443	2,892	7,174	25	25	119.994039	null	benign
1,488,877,717,000,000	152.163.66.165	192.168.10.14	443	49,547	12	0	2	0	0.000003	null	benign
1,488,877,726,000,000	192.168.10.14	192.35.249.120	49,751	443	59	0	2	0	0.000004	null	benign
1,488,877,612,000,000	192.168.10.14	8.43.72.98	49,801	443	1,091	3,567	21	18	118.191509	null	benign
1,488,877,618,000,000	192.168.10.16	172.217.12.206	44,766	80	1,740	2,984	22	17	116.565072	null	benign
1,488,877,737,000,000	191.237.24.89	192.168.10.16	443	34,434	0	0	2	0	0.000003	null	benign
1,488,877,612,000,000	192.168.10.17	45.76.244.202	123	123	96	96	2	2	67.077302	null	benign
1,488,877,616,000,000	192.168.10.50	208.75.89.4	123	123	96	96	2	2	69.076986	null	benign
1,488,877,619,000,000	192.168.10.3	192.168.10.1	62,110	53	43	59	1	1	0.030571	null	benign
1,488,877,593,000,000	192.168.10.3	192.168.10.1	61,919	53	152	584	3	3	25.721377	null	benign
1,488,877,612,000,000	192.168.10.3	192.168.10.1	61,539	53	56	116	1	1	0.030661	null	benign
1,488,877,588,000,000	192.168.10.14	192.168.10.3	64,575	53	142	388	4	4	15.761325	null	benign
1,488,877,772,000,000	172.217.11.14	192.168.10.14	443	49,562	12	0	2	0	0.000003	null	benign
1,488,877,535,000,000	192.168.10.25	192.168.10.255	138	138	2,592	0	12	0	0.000021	null	benign
1,488,877,592,000,000	192.168.10.3	192.168.10.1	60,148	53	56	112	1	1	0.033375	null	benign
1,488,877,577,000,000	192.168.10.3	192.168.10.1	62,171	53	66	237	1	1	0.127071	null	benign
1,488,877,798,000,000	172.217.11.2	192.168.10.16	443	54,528	0	0	2	0	0.000003	null	benign
1,488,877,809,000,000	192.168.10.12	72.21.81.48	40,104	443	64	0	4	0	0.000663	null	benign
1,488,877,696,000,000	192.168.10.5	192.168.10.3	123	123	288	288	6	6	65.921627	null	benign
1,488,877,836,000,000	192.168.10.5	134.170.165.251	49,215	443	144,962	6,636	35	56	5.600788	null	benign
1,488,877,852,000,000	104.88.7.7	192.168.10.14	443	49,958	37	6	2	1	0.000225	null	benign
1,488,877,858,000,000	192.229.163.213	192.168.10.9	443	1,209	43	6	3	1	0.000145	null	benign
1,488,877,334,000,000	192.168.10.3	192.168.10.1	62,369	53	69	200	1	1	0.0616	null	benign
1,488,877,611,000,000	192.168.10.3	192.168.10.1	61,169	53	55	120	1	1	0.030701	null	benign
1,488,877,861,000,000	172.217.7.227	192.168.10.9	443	1,280	50	6	3	1	0.008662	null	benign
1,488,877,866,000,000	192.168.10.16	178.255.83.1	41,750	80	437	859	3	4	0.048602	null	benign
1,488,877,870,000,000	192.168.10.9	68.67.178.199	1,241	443	12	0	2	0	0.000003	null	benign
1,488,877,875,000,000	23.210.202.57	192.168.10.14	443	49,899	43	0	3	0	0.000004	null	benign
1,488,877,612,000,000	192.168.10.14	192.168.10.3	53,766	53	120	376	2	2	0.000263	null	benign
1,488,877,748,000,000	192.168.10.50	69.89.207.99	123	123	96	96	2	2	67.052174	null	benign
1,488,877,753,000,000	192.168.10.50	204.2.134.163	123	123	96	96	2	2	66.094198	null	benign
1,488,877,764,000,000	192.168.10.17	208.75.89.4	123	123	96	96	2	2	69.077427	null	benign
1,488,877,779,000,000	192.168.10.17	38.88.18.251	123	123	96	96	2	2	66.088474	null	benign
1,488,877,758,000,000	192.168.10.14	93.184.216.182	49,897	443	880	5,650	14	13	117.245184	null	benign
1,488,877,945,000,000	103.247.220.132	192.168.10.14	443	50,003	37	0	2	0	0.000001	null	benign
1,488,877,949,000,000	192.168.10.12	220.243.227.213	42,702	443	377	5,409	9	7	0.163802	null	benign
1,488,877,952,000,000	192.168.10.14	172.217.8.3	50,074	443	1,757	4,943	13	11	0.207854	null	benign
1,488,877,949,000,000	192.168.10.14	163.171.72.197	50,039	80	12	0	3	1	6.02214	null	benign
1,488,877,950,000,000	192.168.10.14	163.171.73.6	50,055	80	357	49,031	37	36	7.150913	null	benign
1,488,877,949,000,000	192.168.10.14	163.171.72.197	50,034	80	4,334	447,799	313	304	13.010827	null	benign
1,488,877,949,000,000	192.168.10.14	103.247.220.135	50,037	443	1,601	5,795	12	14	18.388125	null	benign
1,488,877,860,000,000	192.168.10.9	72.21.91.29	1,230	80	1,389	3,196	19	18	115.295422	null	benign
1,488,877,994,000,000	192.168.10.51	162.213.33.44	35,789	443	703	3,950	9	11	0.813853	null	benign

End of preview. Expand in Data Studio

CIC-IDS-2017 — Canonical NetFlow (flowprep)

A ~101k-row slice of the CIC-IDS-2017 intrusion-detection dataset, normalized into a single clean, typed, unit-normalized canonical NetFlow parquet table by flowprep — the open-source flow canonicalization tool DeepTempo runs in production ahead of inference.

This is a conversion proof-point: it shows what a real, messy research dataset looks like after flowprep canonicalize resolves its vendor column names, infers its units, and types its columns. The output is ordinary parquet — pandas/polars/DuckDB/Spark read it natively, the file format is the API.

⚠️ This is a small demonstration slice, not the full CIC-IDS-2017 dataset. For research, obtain the complete dataset from UNB CIC and cite accordingly (see 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):

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, 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

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)

# or with the datasets library
from datasets import load_dataset
ds = load_dataset("DeepTempo/cic-ids-2017-flowprep", split="train")

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.

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 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.

Citation

If you use this data, cite the original CIC-IDS-2017 paper:

@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:

@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 by DeepTempo.

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