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:
| 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. | |