Datasets:
Tasks:
Tabular Classification
Modalities:
Tabular
Sub-tasks:
tabular-multi-class-classification
Languages:
English
Size:
1M<n<10M
Tags:
anomaly-detection
continual-learning
continual-anomaly-detection
network-intrusion-detection
cybersecurity
tabular
License:
File size: 4,188 Bytes
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pretty_name: CAD-CICUNSW
license: cc-by-4.0
language:
- en
task_categories:
- tabular-classification
task_ids:
- tabular-multi-class-classification
size_categories:
- 1M<n<10M
tags:
- anomaly-detection
- continual-learning
- continual-anomaly-detection
- network-intrusion-detection
- cybersecurity
- tabular
- cic-unsw-nb15
---
# CAD-CICUNSW
## Dataset Summary
**CAD-CICUNSW** is a single-source continual anomaly detection benchmark scenario for network intrusion detection. It is derived from **CIC-UNSW-NB15 / UNSW-NB15** and converts the original tabular network-intrusion data into a sequence of concept-grouped tasks.
The dataset contains **1,084,928 samples**, **5 tasks**, and has a reported **12.76% anomaly ratio in the test set**.
The dataset is anonymized for double-blind NeurIPS review. Author names, institutional affiliations, project acknowledgements, and non-anonymous paper references are intentionally omitted.
## Intended Use
This dataset is intended for research on:
- continual anomaly detection;
- continual learning for tabular data;
- network intrusion detection;
- robustness under distribution shift;
- task ordering in continual-learning benchmarks;
- forgetting and knowledge transfer across related network-traffic concepts;
- benchmarking anomaly detectors under sequential task exposure.
The intended use is **defensive machine learning research**. The dataset should not be used to support offensive cybersecurity activity.
## Dataset Source
- CIC-UNSW-NB15: `https://www.unb.ca/cic/datasets/cic-unsw-nb15.html`
- UNSW-NB15: `https://research.unsw.edu.au/projects/unsw-nb15-dataset`
## Dataset Files
The repository contains the following files:
| File | Description |
|---|---|
| `data.csv` | Main tabular dataset file. |
| `orderings.json` | Predefined task orderings for continual-learning evaluation. |
| `croissant.json` | Croissant metadata describing the dataset. |
## Dataset Structure
The main file is:
```text
data.csv
```
The dataset contains task metadata, binary labels, and numerical flow-level features.
### Core Columns
| Column | Type | Description |
|---|---:|---|
| `task_id` | integer | Numeric identifier of the continual-learning task. |
| `task_name` | string | Name of the task, e.g. `cicunsw_0`. |
| `task_split` | string | Split assignment for the row. |
| `label` | integer | Binary anomaly label. Conventionally, `0` denotes benign/normal traffic and `1` denotes anomalous/attack traffic. |
### Task Identifiers
The dataset contains the following task identifiers:
`cicunsw_0`, `cicunsw_1`, `cicunsw_2`, `cicunsw_3`, `cicunsw_4`
### Feature Columns
The remaining columns are numerical network-flow features, including packet-count, byte-count, flag-count, duration, inter-arrival-time, and aggregate flow-statistics features. Representative examples include:
- `Src Port`
- `Dst Port`
- `Protocol`
- `Flow Duration`
- `Tot Fwd Pkts`
- `Tot Bwd Pkts`
- `TotLen Fwd Pkts`
- `TotLen Bwd Pkts`
- `Fwd Pkt Len Mean`
- `Bwd Pkt Len Mean`
- `Flow Byts/s`
- `Flow Pkts/s`
- `Flow IAT Mean`
- `Fwd IAT Mean`
- `Bwd IAT Mean`
For the complete schema, see `croissant.json`.
## Task Orderings
The dataset provides six predefined orderings in `orderings.json`. These orderings define different continual-learning evaluation regimes over the same task set.
| Ordering | Task sequence |
|---|---|
| `curriculum_asc` | `cicunsw_4` → `cicunsw_1` → `cicunsw_3` → `cicunsw_2` → `cicunsw_0` |
| `curriculum_desc` | `cicunsw_0` → `cicunsw_2` → `cicunsw_3` → `cicunsw_1` → `cicunsw_4` |
| `generalization_desc` | `cicunsw_4` → `cicunsw_1` → `cicunsw_2` → `cicunsw_3` → `cicunsw_0` |
| `generalization_asc` | `cicunsw_0` → `cicunsw_3` → `cicunsw_2` → `cicunsw_1` → `cicunsw_4` |
| `smooth_drift` | `cicunsw_4` → `cicunsw_2` → `cicunsw_1` → `cicunsw_3` → `cicunsw_0` |
| `abrupt_drift` | `cicunsw_1` → `cicunsw_2` → `cicunsw_3` → `cicunsw_0` → `cicunsw_4` |
These orderings are intended to expose complementary continual-learning dynamics, including curriculum-like adaptation, generalization-oriented ordering, smooth drift, and abrupt drift.
|