Update README.md

README.md

@@ -2,4 +2,197 @@
 license: mit
 ---
 
-This dataset is created from CSE-CIC-IDS2018 dataset [Read](https://registry.opendata.aws/cse-cic-ids2018/)
+# Network Traffic Anomaly Dataset
+
+A processed and curated dataset for **network traffic anomaly detection**, derived from the **CSE‑CIC‑IDS2018** intrusion detection dataset.  
+This dataset is designed for machine learning and deep learning research on **network security, intrusion detection, and anomaly detection**.
+
+Dataset link: https://huggingface.co/datasets/abmallick/network-traffic-anomaly
+
+> **Important:**  
+> This dataset is created from the **CSE‑CIC‑IDS2018 dataset** and restructured to be easier to use for modern ML workflows.
+
+---
+
+## 📊 Dataset Overview
+
+- **Name:** Network Traffic Anomaly Dataset  
+- **Author:** Abhinav Mallick  
+- **Source Dataset:** CSE‑CIC‑IDS2018  
+- **Domain:** Network Security / Intrusion Detection  
+- **Modalities:** Tabular  
+- **Format:** Parquet  
+- **License:** MIT  
+
+---
+
+## 🧠 Motivation
+
+Raw intrusion detection datasets like CSE‑CIC‑IDS2018 are large, fragmented across multiple files, and often difficult to use directly for ML experiments.
+
+This dataset was created to:
+- Simplify access to IDS2018 data  
+- Provide a clean, ML‑ready format  
+- Enable rapid experimentation for anomaly detection models  
+- Support both classical ML and deep learning pipelines  
+
+It is suitable for **binary anomaly detection** as well as **multi‑class attack classification**.
+
+---
+
+## 📦 Source Dataset: CSE‑CIC‑IDS2018
+
+The original **CSE‑CIC‑IDS2018** dataset was created by the Canadian Institute for Cybersecurity (CIC) and contains realistic benign and malicious network traffic captured over multiple days.
+
+Key characteristics of the original dataset:
+- Realistic enterprise network traffic
+- Multiple attack categories (DoS, DDoS, brute force, infiltration, botnet, etc.)
+- Flow‑based statistical features extracted using CICFlowMeter
+
+This Hugging Face dataset is a **processed and consolidated version** of that data.
+
+---
+
+## 📋 Features / Columns
+
+Each row represents a **network flow** with extracted statistical features.
+
+Typical feature categories include:
+- Flow duration and packet counts  
+- Forward and backward packet statistics  
+- Packet length statistics  
+- Inter‑arrival times  
+- Header and flag features  
+- Byte and packet rate metrics  
+
+### Key Columns
+
+| Column | Description |
+|------|------------|
+| `label` | Target label (benign / attack or anomaly class) |
+| `attack_type` | Specific attack category (if available) |
+| `flow_duration` | Duration of the network flow |
+| `total_fwd_packets` | Total forward packets |
+| `total_bwd_packets` | Total backward packets |
+| `flow_bytes_per_sec` | Bytes transferred per second |
+| `flow_packets_per_sec` | Packets per second |
+| `packet_length_mean` | Mean packet length |
+| `packet_length_std` | Packet length standard deviation |
+| `iat_mean` | Mean inter‑arrival time |
+| `iat_std` | Inter‑arrival time standard deviation |
+| `split` | Dataset split (`train` / `val` / `test`) |
+
+> Exact columns may vary depending on preprocessing and feature selection.
+
+---
+
+## 🧩 Labels
+
+Depending on usage, labels can be interpreted as:
+
+### Binary Classification
+- **0:** Benign traffic  
+- **1:** Anomalous / Malicious traffic  
+
+### Multi‑Class Classification
+- Benign  
+- DoS / DDoS  
+- Brute Force  
+- Botnet  
+- Infiltration  
+- Web attacks  
+- Other attack types  
+
+Users are free to remap labels based on their modeling needs.
+
+---
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+pip install datasets pandas pyarrow
+```
+
+### Load Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "abmallick/network-traffic-anomaly",
+    split="train"
+)
+
+print(dataset[0])
+```
+
+### Convert to Pandas
+
+```python
+df = dataset.to_pandas()
+df.head()
+```
+
+---
+
+## 📈 Example Use Cases
+
+- Network intrusion detection systems (IDS)  
+- Anomaly detection using autoencoders or isolation forests  
+- Supervised attack classification models  
+- Benchmarking ML models on real‑world network traffic  
+- Security analytics and SOC research  
+
+---
+
+## 🧪 Suggested Evaluation Metrics
+
+- Accuracy  
+- Precision / Recall  
+- F1‑score  
+- ROC‑AUC  
+- False Positive Rate (critical for IDS systems)  
+
+---
+
+## 📚 Citation
+
+If you use this dataset, please cite both this dataset and the original source:
+
+### This Dataset
+
+```bibtex
+@misc{mallick2025networktraffic,
+  title={Network Traffic Anomaly Dataset},
+  author={Mallick, Abhinav},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/abmallick/network-traffic-anomaly}
+}
+```
+
+### Original Dataset (CSE‑CIC‑IDS2018)
+
+```bibtex
+@dataset{cse_cic_ids2018,
+  title={CSE-CIC-IDS2018: A Large Scale Dataset for Intrusion Detection Systems},
+  author={Sharafaldin, Iman and Lashkari, Arash Habibi and Ghorbani, Ali A.},
+  year={2018},
+  publisher={Canadian Institute for Cybersecurity}
+}
+```
+
+---
+
+## 📄 License
+
+This dataset is released under the **MIT License**.  
+The original CSE‑CIC‑IDS2018 dataset is subject to its own licensing terms.
+
+---
+
+## 🤝 Contributions
+
+Feedback, issues, and improvements are welcome via the Hugging Face dataset page.