Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+- recall
+tags:
+- >-
+  xgboost - onnx - cybersecurity - ddos - intrusion-detection - network-security
+  - binary-classification
+datasets:
+  - CIC-DDoS2019
+pipeline_tag: tabular-classification
+base_model: "null"
+---
+
+
+# 🛡️ Model Card: DDoS Detection using XGBoost (ONNX)
+
+A high-performance model to detect **DDoS attacks** from network traffic flow data. Trained on [CIC-DDoS2019](https://www.kaggle.com/datasets/dhoogla/cicddos2019), optimized with **Optuna**, and exported to **ONNX** for fast, portable inference.
+
+---
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Sunny Thakur 
+- **Model type:** Gradient Boosted Tree (XGBoost)
+- **Language(s):** Not NLP-specific; flow data in numeric format
+- **License:** MIT
+- **Finetuned from model:** None (Trained from scratch)
+
+### Model Sources
+
+- **Repository:** https://github.com/SunnyThakur25/DDoS-Detection-XGBoost
+- **Demo:** Coming soon
+- **Paper:** N/A (model based on CIC-DDoS2019 dataset)
+
+---
+
+## Uses
+
+### Direct Use
+
+- Detect DDoS attacks from structured flow data (CSV, Parquet, JSONL after transformation)
+- Ideal for cybersecurity monitoring systems, SOC pipelines, or SIEM integrations
+
+### Downstream Use
+
+- Can be integrated in larger threat detection systems
+- Extended to multi-class detection or traffic categorization
+
+### Out-of-Scope Use
+
+- Real-time packet-level classification without flow aggregation
+- NLP, audio, or image data tasks
+
+---
+
+## Bias, Risks, and Limitations
+
+- Model may overfit synthetic DDoS traffic patterns
+- Limited to features available in CIC-DDoS2019
+- SMOTE oversampling may create synthetic minority patterns that don't generalize
+
+### Recommendations
+
+- Validate on real-world or updated datasets before deployment
+- Periodic retraining recommended as attack patterns evolve
+
+---
+
+## How to Get Started with the Model
+
+### ONNX Inference (Python)
+
+```python
+import onnxruntime as ort
+import numpy as np
+
+session = ort.InferenceSession("ddos_model.onnx")
+input_data = np.array([...], dtype=np.float32).reshape(1, -1)
+outputs = session.run(None, {"input": input_data})
+```
+
+
+
+```python
+
+import joblib
+pipeline = joblib.load("ddos_detection_pipeline.pkl")
+prediction = pipeline.predict(input_df)
+```
+
+
+Training Details
+Training Data
+
+    Dataset: CIC-DDoS2019
+
+    Source: https://www.kaggle.com/datasets/dhoogla/cicddos2019
+
+    Classes: Binary (Benign vs DDoS)
+
+Training Procedure
+
+    Preprocessed: IPs, ports, timestamps dropped
+
+    Feature engineered: requests_per_sec, pkt_len_variation
+
+    Balancing: SMOTE (30% oversample minority)
+
+    Scaler: StandardScaler
+
+    Model: XGBoost
+
+    Optimized using Optuna (F1-score, 30 trials)
+
+Training Hyperparameters
+
+    n_estimators: 100–500 (tuned)
+
+    max_depth: 3–12 (tuned)
+
+    learning_rate: 0.001–0.2
+
+    gamma, colsample_bytree, scale_pos_weight: tuned
+
+    tree_method: hist
+
+    early_stopping_rounds: 20
+
+Evaluation
+Testing Data
+
+    20% hold-out split from full data
+
+    Stratified on class label
+
+| Metric    | Value  |
+| --------- | ------ |
+| Accuracy  | 99.98% |
+| F1-Score  | 99.98% |
+| AUC-PR    | 1.000  |
+| Precision | \~1.00 |
+| Recall    | \~1.00 |
+
+
+Model Examination
+Explainability
+
+    Integrated SHAP
+
+    Summary plots identify dominant flow-level features
+    
+Environmental Impact
+
+    Hardware: Kaggle Tesla T4 or P100
+
+    Training Time: < 1 hour
+
+    Carbon Emitted: Low (can be estimated via ML CO2 Impact)
+
+Technical Specifications
+
+    Architecture: Gradient Boosted Trees (XGBoost)
+
+    Format: ONNX + .pkl pipeline
+
+    Dependencies: XGBoost, ONNX, Scikit-learn, Optuna, SHAP
+
+
+
+Citation
+```java
+@misc{ddos-detection-xgboost-007,
+  author = {Sunny Thakur (007)},
+  title = {DDoS Detection Model - CICDDoS2019 - XGBoost + ONNX},
+  year = {2025},
+  url = {https://huggingface.co/darkknight25/ddos_xgboost_onnx}
+}
+```
+Model Card Authors
+
+    Sunny Thakur 
+
+Contact
+
+    GitHub: SunnyThakur25
+
+    LinkedIn: Sunny thakur