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.

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.