| --- |
| 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](https://www.unb.ca/cic/datasets/ddos-2019.html) |
| 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](https://github.com/doriguzzi/flad-federated-learning-ddos) 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"](https://arxiv.org/abs/2205.06661) |
