Datasets:

AINovice2005
/

cicflow-ids-binary

dataset_info:
  features:
    - name: flow_id
      dtype: string
    - name: features
      struct:
        - name: src_port
          dtype: float32
        - name: dst_port
          dtype: float32
        - name: protocol
          dtype: float32
        - name: flow_duration
          dtype: float32
        - name: total_fwd_packet
          dtype: float32
        - name: total_bwd_packets
          dtype: float32
        - name: total_length_of_fwd_packet
          dtype: float32
        - name: total_length_of_bwd_packet
          dtype: float32
        - name: fwd_packet_length_max
          dtype: float32
        - name: fwd_packet_length_min
          dtype: float32
        - name: fwd_packet_length_mean
          dtype: float32
        - name: fwd_packet_length_std
          dtype: float32
        - name: bwd_packet_length_max
          dtype: float32
        - name: bwd_packet_length_min
          dtype: float32
        - name: bwd_packet_length_mean
          dtype: float32
        - name: bwd_packet_length_std
          dtype: float32
        - name: flow_bytes_per_s
          dtype: float32
        - name: flow_packets_per_s
          dtype: float32
        - name: flow_iat_mean
          dtype: float32
        - name: flow_iat_std
          dtype: float32
        - name: flow_iat_max
          dtype: float32
        - name: flow_iat_min
          dtype: float32
        - name: fwd_iat_total
          dtype: float32
        - name: fwd_iat_mean
          dtype: float32
        - name: fwd_iat_std
          dtype: float32
        - name: fwd_iat_max
          dtype: float32
        - name: fwd_iat_min
          dtype: float32
        - name: bwd_iat_total
          dtype: float32
        - name: bwd_iat_mean
          dtype: float32
        - name: bwd_iat_std
          dtype: float32
        - name: bwd_iat_max
          dtype: float32
        - name: bwd_iat_min
          dtype: float32
        - name: fwd_psh_flags
          dtype: float32
        - name: bwd_psh_flags
          dtype: float32
        - name: fwd_urg_flags
          dtype: float32
        - name: bwd_urg_flags
          dtype: float32
        - name: fwd_header_length
          dtype: float32
        - name: bwd_header_length
          dtype: float32
        - name: fwd_packets_per_s
          dtype: float32
        - name: bwd_packets_per_s
          dtype: float32
        - name: packet_length_min
          dtype: float32
        - name: packet_length_max
          dtype: float32
        - name: packet_length_mean
          dtype: float32
        - name: packet_length_std
          dtype: float32
        - name: packet_length_variance
          dtype: float32
        - name: fin_flag_count
          dtype: float32
        - name: syn_flag_count
          dtype: float32
        - name: rst_flag_count
          dtype: float32
        - name: psh_flag_count
          dtype: float32
        - name: ack_flag_count
          dtype: float32
        - name: urg_flag_count
          dtype: float32
        - name: cwr_flag_count
          dtype: float32
        - name: ece_flag_count
          dtype: float32
        - name: down_per_up_ratio
          dtype: float32
        - name: average_packet_size
          dtype: float32
        - name: fwd_segment_size_avg
          dtype: float32
        - name: bwd_segment_size_avg
          dtype: float32
        - name: fwd_bytes_per_bulk_avg
          dtype: float32
        - name: fwd_packet_per_bulk_avg
          dtype: float32
        - name: fwd_bulk_rate_avg
          dtype: float32
        - name: bwd_bytes_per_bulk_avg
          dtype: float32
        - name: bwd_packet_per_bulk_avg
          dtype: float32
        - name: bwd_bulk_rate_avg
          dtype: float32
        - name: subflow_fwd_packets
          dtype: float32
        - name: subflow_fwd_bytes
          dtype: float32
        - name: subflow_bwd_packets
          dtype: float32
        - name: subflow_bwd_bytes
          dtype: float32
        - name: fwd_init_win_bytes
          dtype: float32
        - name: bwd_init_win_bytes
          dtype: float32
        - name: fwd_act_data_pkts
          dtype: float32
        - name: fwd_seg_size_min
          dtype: float32
        - name: active_mean
          dtype: float32
        - name: active_std
          dtype: float32
        - name: active_max
          dtype: float32
        - name: active_min
          dtype: float32
        - name: idle_mean
          dtype: float32
        - name: idle_std
          dtype: float32
        - name: idle_max
          dtype: float32
        - name: idle_min
          dtype: float32
    - name: semantic_flags
      struct:
        - name: high_packet_rate
          dtype: int8
        - name: one_way_traffic
          dtype: int8
        - name: syn_ack_imbalance
          dtype: int8
        - name: uniform_packet_size
          dtype: int8
        - name: long_idle_c2
          dtype: int8
    - name: label
      dtype:
        class_label:
          names:
            '0': benign
            '1': attack
  splits:
    - name: train
      num_bytes: 248127049
      num_examples: 671088
    - name: validation
      num_bytes: 62032886
      num_examples: 167772
    - name: test
      num_bytes: 77540185
      num_examples: 209715
  download_size: 119245598
  dataset_size: 387700120
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
task_categories:
  - tabular-classification
language:
  - en
tags:
  - cybersecurity
size_categories:
  - 1M<n<10M

CICFlow Binary Intrusion Detection Dataset

Overview

This dataset provides a binary network intrusion detection (IDS) benchmark derived from CICFlowMeter flow-level features.

The task is to determine whether a given network flow is benign or malicious, making this dataset suitable for:

Practical IDS research
Rule-based and hybrid ML systems
High-imbalance classification experiments
Explainable security modeling

Task

Binary classification

Given a network flow represented by CICFlowMeter features, predict whether it corresponds to an attack.

Labels

Label ID	Name	Description
0	benign	Normal network traffic
1	attack	Any malicious traffic (all attack types merged)

Label construction

The binary label was derived by collapsing a multiclass IDS taxonomy:

benign → 0
all attack categories → 1

No other transformations were applied to the labels.

Dataset Structure

DatasetDict({
train,
validation,
test
})

Each split contains records with the following schema:

flow_id: string
features: dict[str, float]
semantic_flags: dict[str, int]
label: ClassLabel (benign / attack)

Feature Description

1. Raw Numeric Features (`features`)

The features field contains flow-level statistics extracted by CICFlowMeter.

These include:

Traffic volume & direction

total_fwd_packets, total_bwd_packets
total_length_of_fwd_packets
total_length_of_bwd_packets
down_up_ratio

Packet size statistics

packet_length_min, packet_length_max
packet_length_mean, packet_length_std, packet_length_variance
Forward and backward packet length metrics

Timing & inter-arrival times

flow_duration
flow_iat_mean, flow_iat_std, flow_iat_max, flow_iat_min
Forward and backward IAT statistics
active_*, idle_* features

Rate-based features

flow_bytes_per_s
flow_packets_per_s
fwd_packets_per_s, bwd_packets_per_s

TCP flag counters

syn_flag_count, ack_flag_count, rst_flag_count
fin_flag_count, psh_flag_count, urg_flag_count
cwr_flag_count, ece_flag_count

Bulk and subflow statistics

*_bulk_* features
subflow_fwd_*, subflow_bwd_*

Note: Some CICFlowMeter features are conditionally emitted. Missing or undefined numeric values were filled with 0.0, which semantically indicates absence of that behavior.

2. Semantic Flags (`semantic_flags`)

Each flow includes deterministic, interpretable indicators derived from numeric features:

Flag	Meaning
`high_packet_rate`	Extremely high packets per second
`one_way_traffic`	Forward-only traffic (no backward response)
`syn_ack_imbalance`	Large SYN/ACK imbalance
`uniform_packet_size`	Low packet size variance
`long_idle_c2`	Long idle periods (possible command-and-control behavior)

These flags are:

Deterministic
Stable across dataset versions
Suitable for rule-based IDS and hybrid systems

Preprocessing Summary

The dataset was generated using a fully deterministic preprocessing pipeline:

Column name normalization
Conversion of NaN / ±Infinity → 0.0
Explicit label normalization
No row removal due to missing numeric values
Stratified train/validation/test splits
Explicit Hugging Face feature schemas

No rebalancing, oversampling, or augmentation was applied.

Class Imbalance

The dataset is highly imbalanced, with benign traffic dominating.

This reflects real-world network conditions and is intentional.

Users are encouraged to:

Use precision, recall, F1, PR-AUC
Apply class weighting or cost-sensitive learning
Avoid relying on accuracy alone