Datasets:

anonymizeddb
/

CAD-CICUNSW

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:

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.