Datasets:
Tasks:
Tabular Classification
Modalities:
Text
Languages:
English
Size:
1M - 10M
Tags:
cybersecurity
| 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. | |
| --- | |