init5iv3/network-traffic-detection

Network Traffic Detection Dataset

A curated dataset for network traffic anomaly detection, derived from the BCCC-CSE-CIC-IDS2018 intrusion detection dataset, which itself is an enhanced version of CSE-CIC-IDS2018
This dataset is restructured for modern machine learning and deep learning research on network security and intrusion detection.

The raw dataset (~90GB) was fragmented across 34 .csv files. To optimize for ML workflows, the data was merged and compressed into .parquet chunks using the polars Python library.
During the merge, 18 columns exhibited data-type mismatches (e.g., containing both float64 values and strings like "not a complete handshake"). To resolve this, these specific columns were type-cast to Utf8.

• View Schema Comparison Output
• View Merge Script

Usage

This dataset is large (~90GB uncompressed).
Attempting to load the entire dataset into memory at once (e.g., via pd.read_parquet() or dataset.to_pandas()) will cause Out-of-Memory (OOM) crashes on some machines.
To safely use this dataset, separate the download step from the ingestion step, and process the data using lazy evaluation or batching.

Accessing & Downloading the Data

Choose the method that best fits your system's constraints:
Hugging Face docs on downloading datasets

datasets
downloads the dataset using Apache Arrow

from datasets import load_dataset

dataset = load_dataset("init5iv3/network-traffic-detection", split="train")

streaming with datasets
Iterate through the data over the network without downloading the entire dataset to disk

from datasets import load_dataset

dataset = load_dataset("init5iv3/network-traffic-detection", split="train", streaming=True)

local download via CLI
download .parquet files to a local directory

hf download --quiet init5iv3/network-traffic-detection --repo-type dataset --local-dir /path/to/dir

local download via snapshot_download
download .parquet files to a local directory

from huggingface_hub import snapshot_download

snapshot_download(
  repo_id="init5iv3/network-traffic-detection",
  repo_type="dataset",
  local_dir="/path/to/dir",
  allow_patterns="*.parquet",
  resume_download=True
)

virtual mounting with hf-mount

hf-mount start repo init5iv3/network-traffic-detection /path/to/local/mount

Ingesting/Processing

polars
if the dataset was downloaded locally, polars can lazily scan the fragmented .parquet files in chunks

import polars as pl

df_lazy = pl.scan_parquet("/path/to/dir/**/*.parquet")

label_counts = df_lazy.select("label").value_counts().collect()

batch-processing with datasets library

from datasets import load_dataset

dataset = load_dataset("init5iv3/network-traffic-detection", split="train")

for batch in dataset.iter(batch_size=10000):
  # process features here.
  pass

Overview

Raw intrusion detection datasets like BCCC-CSE-CIC-IDS2018 are large, fragmented across multiple files, and often difficult to use directly for ML experiments.
This dataset was created to provide an ML-ready format to the raw data, containing over 300 network flow features extracted via NTLFlowLyzer.
It is suitable for binary and multi-class classification.

Labels

Binary Classification

1. Benign
2. Non-benign: Aggregated from all specific attack classes below

Multi‑Class Classification

• Benign
• DoS-Hulk
• DoS-Slowhttptest
• DoS-GoldenEye
• DoS-Slowloris
• DDoS-LOIC
• DDoS-HOIC
• Brute-Force-XSS
• Brute-Force-Web
• Brute-Force-FTP
• Brute-Force-SSH
• SQL-Injection
• Botnet
• Infiltration

Citation

If you use this dataset, please cite both this repository and the original source:

This dataset

@misc{init5iv32026networktraffic,
  title={Network Traffic Dataset},
  author={init5iv3},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/init5iv3/network-traffic-detection}
}

Original dataset (BCCC-CSE-CIC-IDS2018) and paper

@misc{bccc_cse_cic_ids2018_dataset,
  title={BCCC-CSE-CIC-IDS2018: Large-Scale Intrusion Detection Dataset},
  author={Shafi, MohammadMoein and Lashkari, Arash Habibi and Roudsari, Arousha Haghighian},
  year={2025},
  publisher={Behaviour-Centric Cybersecurity Center - York University},
  url={https://www.yorku.ca/research/bccc/ucs-technical/cybersecurity-datasets-cds/large-scale-intrusion-detection-dataset-bccc-cse-cic-ids2018/}
}

@article{shafi2025toward,
  title={Toward Generating a Large Scale Intrusion Detection Dataset and Intruders Behavioral Profiling Using Network and Transportation Layers Traffic Flow Analyzer (NTLFlowLyzer)},
  author={Shafi, MohammadMoein and Lashkari, Arash Habibi and Roudsari, Arousha Haghighian},
  journal={Journal of Network and Systems Management},
  volume={33},
  number={44},
  year={2025},
  publisher={Springer}
}

License

This dataset is released under the MIT License.
The original BCCC-CSE-CIC-IDS2018 dataset is subject to its own licensing terms.

Contributions

Feedback is welcome via the community page