README.md

metadata

license: other
license_name: cic-ddos2019-derivative
license_link: LICENSE
task_categories:
  - tabular-classification
tags:
  - network-traffic
  - ddos
  - intrusion-detection
  - federated-learning
size_categories:
  - 1M<n<10M
pretty_name: CIC-DDoS2019 Highly Unbalanced

CIC-DDoS2019 Highly Unbalanced (Federated, Pre-Partitioned)

This dataset is a preprocessed, repartitioned derivative of the CIC-DDoS2019 dataset, originally published by the Canadian Institute for Cybersecurity (CIC), University of New Brunswick. More details about the CIC-DDoS2019 dataset can be found on this page and in the following scientific paper:

Iman Sharafaldin, Arash Habibi Lashkari, Saqib Hakak, and Ali A. Ghorbani, "Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy", IEEE 53rd International Carnahan Conference on Security Technology, Chennai, India, 2019.

This version contains network flows partitioned per attack-type/client_id, converted from HDF5 to Parquet, and consolidated into three splits (train, val, test) for use in FLAD federated learning experiments.

Dataset structure

Each split (train.parquet - ~80% of the whole dataset, val.parquet - ~10%, test.parquet - ~10%) contains one row per network flow sample, with the following columns:

Column	Type	Description
client_id	string	Attack-type / client partition the sample belongs to (e.g. "11-NetBIOS")
features	list<float32>	Flattened feature window; reshape to (10, 11) per sample to restore the original layout
label	int64	0 = benign, 1 = malicious

Samples are grouped by client_id, mirroring the original per-client HDF5 directory structure, used in the FLAD experimentation. The original HDF5 structure comprises 13 clients X 3 splits = 39 HDF5 files in the form of arrays of shape n = 10 rows and f = 11 columns. The 11 features are the following:

Time, Packet Length, Highest Protocol, IP Flags, Protocols, TCP Length, TCP Ack, TCP Flags, TCP Window Size, UDP Length and ICMP Type.

Each client contains samples of benign traffic and only one type of attack. Although each group has been balanced to ensure an approximately equal distribution between benign and DDoS samples, the partition across groups/clients is strongly non-i.i.d since each one represents a single attack type.

Related Work

This dataset has been used in:

Roberto Doriguzzi-Corin, Domenico Siracusa, "FLAD: Adaptive Federated Learning for DDoS attack detection"