---
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.