Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,122 @@
+---
+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.