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.

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:

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.