Update README.md

# 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.*

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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.

---

## Dataset Structure

```text
DatasetDict({
train,
validation,
test
})
```

Each split contains records with the following schema:

```text
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.

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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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README.md

@@ -202,4 +202,12 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
----
+task_categories:
+- tabular-classification
+language:
+- en
+tags:
+- cybersecurity
+size_categories:
+- 1M<n<10M
+---