MCAD-CIC-3xN

Dataset Summary

MCAD-CIC-3xN is a multi-source continual anomaly detection benchmark scenario for network intrusion detection. It combines three CIC-family source datasets into a 13-task continual-learning scenario:

1. CIC-IDS2017-derived tasks;
2. CIC-IDS2018-derived tasks;
3. CIC-UNSW-NB15-derived tasks.

Unlike a single-task-per-source construction, this benchmark provides multiple concept-grouped tasks per source dataset. It is intended to evaluate continual anomaly detection methods under both intra-source task variation and cross-source distribution shift.

The dataset contains 3,581,792 samples, 13 tasks, and has a reported 27.36% 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 anomaly detection datasets;
• 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 Sources

MCAD-CIC-3xN is derived from the following source datasets:

• CIC-IDS2017: https://www.unb.ca/cic/datasets/ids-2017.html
• CIC-IDS2018: https://www.unb.ca/cic/datasets/ids-2018.html
• CIC-UNSW-NB15: https://www.unb.ca/cic/datasets/cic-unsw-nb15.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, cicids2018_2, or cicunsw_3.
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 13 tasks are grouped by source dataset:

Source group	Task identifiers
CIC-IDS2017	cicids2017_0, cicids2017_1, cicids2017_2, cicids2017_3, cicids2017_4
CIC-IDS2018	cicids2018_0, cicids2018_1, cicids2018_2, cicids2018_3
CIC-UNSW-NB15	cicunsw_0, cicunsw_1, cicunsw_2, cicunsw_3

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:

• 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
• Packet Length Mean
• Packet Length Std
• Packet Length Variance
• SYN Flag Count
• ACK Flag Count
• RST Flag Count
• PSH Flag Count
• Destination Port

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 13 tasks.

Ordering	Task sequence
curriculum_asc	cicunsw_3 → cicids2017_1 → cicids2017_4 → cicids2017_0 → cicunsw_1 → cicids2017_2 → cicids2018_2 → cicids2017_3 → cicunsw_0 → cicunsw_2 → cicids2018_3 → cicids2018_1 → cicids2018_0
curriculum_desc	cicids2018_0 → cicids2018_1 → cicids2018_3 → cicunsw_2 → cicunsw_0 → cicids2017_3 → cicids2018_2 → cicids2017_2 → cicunsw_1 → cicids2017_0 → cicids2017_4 → cicids2017_1 → cicunsw_3
generalization_desc	cicunsw_3 → cicunsw_1 → cicunsw_0 → cicids2017_4 → cicunsw_2 → cicids2018_0 → cicids2018_2 → cicids2017_1 → cicids2018_1 → cicids2017_3 → cicids2017_2 → cicids2017_0 → cicids2018_3
generalization_asc	cicids2018_3 → cicids2017_0 → cicids2017_2 → cicids2017_3 → cicids2018_1 → cicids2017_1 → cicids2018_2 → cicids2018_0 → cicunsw_2 → cicids2017_4 → cicunsw_0 → cicunsw_1 → cicunsw_3
smooth_drift	cicids2018_2 → cicids2018_3 → cicunsw_0 → cicids2018_1 → cicids2017_4 → cicunsw_1 → cicunsw_3 → cicunsw_2 → cicids2018_0 → cicids2017_0 → cicids2017_3 → cicids2017_1 → cicids2017_2
abrupt_drift	cicunsw_0 → cicids2017_2 → cicunsw_3 → cicids2017_1 → cicunsw_2 → cicids2018_2 → cicids2017_4 → cicids2017_0 → cicunsw_1 → cicids2017_3 → cicids2018_1 → cicids2018_3 → cicids2018_0

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