abmallick/network-traffic-anomaly

Network Traffic Anomaly Dataset

A processed and curated dataset for network traffic anomaly detection, derived from the CSE‑CIC‑IDS2018 intrusion detection dataset.
This dataset is designed for machine learning and deep learning research on network security, intrusion detection, and anomaly detection.

Dataset link: https://huggingface.co/datasets/abmallick/network-traffic-anomaly

Important:
This dataset is created from the CSE‑CIC‑IDS2018 dataset and restructured to be easier to use for modern ML workflows.

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📊 Dataset Overview

• Name: Network Traffic Anomaly Dataset
• Author: Abhinav Mallick
• Source Dataset: CSE‑CIC‑IDS2018
• Domain: Network Security / Intrusion Detection
• Modalities: Tabular
• Format: Parquet
• License: MIT

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🧠 Motivation

Raw intrusion detection datasets like CSE‑CIC‑IDS2018 are large, fragmented across multiple files, and often difficult to use directly for ML experiments.

This dataset was created to:

• Simplify access to IDS2018 data
• Provide a clean, ML‑ready format
• Enable rapid experimentation for anomaly detection models
• Support both classical ML and deep learning pipelines

It is suitable for binary anomaly detection as well as multi‑class attack classification.

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📦 Source Dataset: CSE‑CIC‑IDS2018

The original CSE‑CIC‑IDS2018 dataset was created by the Canadian Institute for Cybersecurity (CIC) and contains realistic benign and malicious network traffic captured over multiple days.

Key characteristics of the original dataset:

• Realistic enterprise network traffic
• Multiple attack categories (DoS, DDoS, brute force, infiltration, botnet, etc.)
• Flow‑based statistical features extracted using CICFlowMeter

This Hugging Face dataset is a processed and consolidated version of that data.

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📋 Features / Columns

Each row represents a network flow with extracted statistical features.

Typical feature categories include:

• Flow duration and packet counts
• Forward and backward packet statistics
• Packet length statistics
• Inter‑arrival times
• Header and flag features
• Byte and packet rate metrics

Key Columns

Column	Description
label	Target label (benign / attack or anomaly class)
attack_type	Specific attack category (if available)
flow_duration	Duration of the network flow
total_fwd_packets	Total forward packets
total_bwd_packets	Total backward packets
flow_bytes_per_sec	Bytes transferred per second
flow_packets_per_sec	Packets per second
packet_length_mean	Mean packet length
packet_length_std	Packet length standard deviation
iat_mean	Mean inter‑arrival time
iat_std	Inter‑arrival time standard deviation
split	Dataset split (train / val / test)

Exact columns may vary depending on preprocessing and feature selection.

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🧩 Labels

Depending on usage, labels can be interpreted as:

Binary Classification

• 0: Benign traffic
• 1: Anomalous / Malicious traffic

Multi‑Class Classification

• Benign
• DoS / DDoS
• Brute Force
• Botnet
• Infiltration
• Web attacks
• Other attack types

Users are free to remap labels based on their modeling needs.

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🚀 Quick Start

Installation

pip install datasets pandas pyarrow

Load Dataset

from datasets import load_dataset

dataset = load_dataset(
    "abmallick/network-traffic-anomaly",
    split="train"
)

print(dataset[0])

Convert to Pandas

df = dataset.to_pandas()
df.head()

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📈 Example Use Cases

• Network intrusion detection systems (IDS)
• Anomaly detection using autoencoders or isolation forests
• Supervised attack classification models
• Benchmarking ML models on real‑world network traffic
• Security analytics and SOC research

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🧪 Suggested Evaluation Metrics

• Accuracy
• Precision / Recall
• F1‑score
• ROC‑AUC
• False Positive Rate (critical for IDS systems)

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📚 Citation

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

This Dataset

@misc{mallick2025networktraffic,
  title={Network Traffic Anomaly Dataset},
  author={Mallick, Abhinav},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/abmallick/network-traffic-anomaly}
}

Original Dataset (CSE‑CIC‑IDS2018)

@dataset{cse_cic_ids2018,
  title={CSE-CIC-IDS2018: A Large Scale Dataset for Intrusion Detection Systems},
  author={Sharafaldin, Iman and Lashkari, Arash Habibi and Ghorbani, Ali A.},
  year={2018},
  publisher={Canadian Institute for Cybersecurity}
}

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📄 License

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

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🤝 Contributions

Feedback, issues, and improvements are welcome via the Hugging Face dataset page.