Datasets:
Tasks:
Tabular Classification
Modalities:
Text
Languages:
English
Size:
1M - 10M
Tags:
cybersecurity
File size: 10,492 Bytes
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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': analysis
'2': backdoor
'3': dos
'4': exploits
'5': fuzzers
'6': generic
'7': reconnaissance
'8': shellcode
'9': worms
- name: is_attack
dtype: int8
splits:
- name: train
num_bytes: 248798843
num_examples: 671088
- name: validation
num_bytes: 62199963
num_examples: 167772
- name: test
num_bytes: 77749889
num_examples: 209715
download_size: 119275343
dataset_size: 388748695
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 Multiclass Intrusion Detection Dataset
## Overview
This dataset provides a **multiclass network intrusion detection (IDS) benchmark** derived from **CICFlowMeter flow-level features**.
It is designed for **attack-type classification**, **robust IDS research**, and **interpretable security modeling**.
Each network flow is labeled as either **benign** or one of **nine attack categories**, following a standard IDS taxonomy.
The dataset is published in **Hugging Face `datasets` format** with explicit schemas, clean preprocessing, and reproducible splits.
---
## Task
**Multiclass classification**
> *Given a network flow represented by CICFlowMeter features, predict the attack category.*
---
## Label Taxonomy
The dataset uses the following **10-class label space**:
| Label ID | Name | Description |
| -------: | -------------- | ------------------------------------------ |
| 0 | benign | Normal network traffic |
| 1 | analysis | Port scans, probing, and analysis activity |
| 2 | backdoor | Backdoor and remote access behavior |
| 3 | dos | Denial-of-Service attacks |
| 4 | exploits | Exploitation of vulnerabilities |
| 5 | fuzzers | Fuzzing and malformed input attacks |
| 6 | generic | Generic attack traffic |
| 7 | reconnaissance | Reconnaissance and information gathering |
| 8 | shellcode | Shellcode execution attempts |
| 9 | worms | Worm propagation traffic |
### Additional label fields
* `label` → multiclass label (0–9)
* `is_attack` → binary indicator (`1` if label ≠ 0, else `0`)
This allows both **multiclass IDS** and **binary detection** experiments without reprocessing.
---
## Dataset Structure
```json
DatasetDict({
train,
validation,
test
})
```
Each split contains records with the following schema:
```json
flow_id: string
features: dict[str, float]
semantic_flags: dict[str, int]
label: ClassLabel (0–9)
is_attack: int (0/1)
```
---
## Feature Description
### 1. Raw Numeric Features (`features`)
The `features` field contains **CICFlowMeter-derived flow statistics**, including:
#### 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 statistics
#### 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_*` metrics
#### 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 (conditionally emitted by CICFlowMeter)
* `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`)
To support **interpretable and rule-based IDS**, each flow includes deterministic semantic indicators:
| Flag | Meaning |
| --------------------- | ---------------------------------------- |
| `high_packet_rate` | Very high packets per second |
| `one_way_traffic` | Forward-only traffic (no response) |
| `syn_ack_imbalance` | Large SYN/ACK imbalance |
| `uniform_packet_size` | Low packet size variance |
| `long_idle_c2` | Long idle periods (possible C2 behavior) |
These flags are:
* Derived deterministically from numeric features
* Stable across dataset versions
* Suitable for rule-based or hybrid ML systems
---
## Preprocessing Summary
The dataset was produced using a **fully deterministic pipeline**:
* Column name normalization
* Conversion of `NaN` / `±Infinity` → `0.0`
* Explicit label normalization (string → numeric)
* No row drops based on missing numeric values
* Stratified train/validation/test splits
* Explicit Hugging Face feature schemas
No synthetic balancing, oversampling, or augmentation was applied.
---
## Class Distribution & Imbalance
The dataset is **highly imbalanced**, reflecting real-world network traffic:
* Benign traffic dominates
* Some attack classes are rare
This is **intentional and realistic**.
Users are strongly encouraged to:
* Use **macro / weighted F1**
* Inspect **per-class recall**
* Apply **class weighting** where appropriate
Accuracy alone is not a meaningful metric for this dataset.
---
## Related Datasets
* A **binary IDS version** of this dataset is published separately for attack detection use cases.
* Both datasets share identical features and preprocessing logic.
---
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