update dataset readme

README.md

@@ -220,4 +220,182 @@ 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.
+
+---