AINovice2005/cicflow-ids-binary

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

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Task

Binary classification

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

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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.

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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)

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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.

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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

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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.

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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

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