Datasets:

anonymizeddb
/

CAD-CICIDS2018

pretty_name: CAD-CICIDS2018
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-ids2018

CAD-CICIDS2018

Dataset Summary

CAD-CICIDS2018 is a single-source continual anomaly detection benchmark scenario for network intrusion detection. It is derived from CSE-CIC-IDS2018 and converts the original tabular network-intrusion data into a sequence of concept-grouped tasks.

The dataset contains 2,590,771 samples, 5 tasks, and has a reported 28.04% 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

CSE-CIC-IDS2018: https://www.unb.ca/cic/datasets/ids-2018.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. `cicids2018_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:

cicids2018_0, cicids2018_1, cicids2018_2, cicids2018_3, cicids2018_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:

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`	`cicids2018_3` → `cicids2018_4` → `cicids2018_2` → `cicids2018_1` → `cicids2018_0`
`curriculum_desc`	`cicids2018_0` → `cicids2018_1` → `cicids2018_2` → `cicids2018_4` → `cicids2018_3`
`generalization_desc`	`cicids2018_2` → `cicids2018_3` → `cicids2018_0` → `cicids2018_1` → `cicids2018_4`
`generalization_asc`	`cicids2018_4` → `cicids2018_1` → `cicids2018_0` → `cicids2018_3` → `cicids2018_2`
`smooth_drift`	`cicids2018_2` → `cicids2018_0` → `cicids2018_1` → `cicids2018_3` → `cicids2018_4`
`abrupt_drift`	`cicids2018_1` → `cicids2018_2` → `cicids2018_3` → `cicids2018_0` → `cicids2018_4`

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