Datasets:

anonymizeddb
/

CAD-CICIDS2017

pretty_name: CAD-CICIDS2017
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-ids2017

CAD-CICIDS2017

Dataset Summary

CAD-CICIDS2017 is a single-source continual anomaly detection benchmark scenario for network intrusion detection. It is derived from CIC-IDS2017 and converts the original tabular network-intrusion data into a sequence of concept-grouped tasks.

The dataset contains 2,076,848 samples, 6 tasks, and has a reported 18.77% 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-IDS2017: https://www.unb.ca/cic/datasets/ids-2017.html

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. `cicids2017_0`.
`task_split`	string	Split assignment for the row.
`label`	integer	Binary anomaly label. `0` denotes benign/normal traffic and `1` denotes anomalous/attack traffic.

Task Identifiers

The dataset contains the following task identifiers:

cicids2017_0, cicids2017_1, cicids2017_2, cicids2017_3, cicids2017_4, cicids2017_5

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:

Destination Port
Flow Duration
Flow Bytes/s
Flow Packets/s
Total Fwd Packets
Total Backward Packets
Total Length of Fwd Packets
Total Length of Bwd Packets
Fwd Packet Length Mean
Bwd Packet Length Mean
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`	`cicids2017_5` → `cicids2017_2` → `cicids2017_0` → `cicids2017_3` → `cicids2017_4` → `cicids2017_1`
`curriculum_desc`	`cicids2017_1` → `cicids2017_4` → `cicids2017_3` → `cicids2017_0` → `cicids2017_2` → `cicids2017_5`
`generalization_desc`	`cicids2017_4` → `cicids2017_3` → `cicids2017_0` → `cicids2017_2` → `cicids2017_5` → `cicids2017_1`
`generalization_asc`	`cicids2017_1` → `cicids2017_5` → `cicids2017_2` → `cicids2017_0` → `cicids2017_3` → `cicids2017_4`
`smooth_drift`	`cicids2017_5` → `cicids2017_1` → `cicids2017_4` → `cicids2017_0` → `cicids2017_2` → `cicids2017_3`
`abrupt_drift`	`cicids2017_4` → `cicids2017_5` → `cicids2017_3` → `cicids2017_1` → `cicids2017_2` → `cicids2017_0`

These orderings are intended to expose complementary continual-learning dynamics, including curriculum-like adaptation, generalization-oriented ordering, smooth drift, and abrupt drift.