Upload README.md

README.md

@@ -1,3 +1,202 @@
----
-license: mit
----
+# Malicious URL Detection Model
+
+> A fine-tuned **BERT-LoRA** model for detecting malicious URLs, including phishing, malware, and defacement threats.
+
+## Model Description
+
+This model is a **fine-tuned BERT-based classifier** designed to detect **malicious URLs** in real-time. It applies **Low-Rank Adaptation (LoRA)** for efficient fine-tuning, reducing computational costs while maintaining high accuracy.
+
+The model classifies URLs into **four categories**:
+
+- ✅ **Benign**
+- 🔴 **Defacement**
+- ⚠️ **Phishing**
+- 🛑 **Malware**
+
+It achieves **98% validation accuracy** and an **F1-score of 0.965**, ensuring robust detection capabilities.
+
+---
+
+## Intended Uses & Limitations
+
+### Use Cases
+
+✔️ Real-time URL classification for cybersecurity tools✔️ Phishing and malware detection for online safety✔️ Integration into browser extensions for instant threat alerts✔️ Security monitoring for SOC (Security Operations Centers)
+
+### Limitations
+
+⚠️ May **misclassify short or obfuscated URLs**⚠️ Performance may degrade with **dynamic domain structures**⚠️ Requires **frequent retraining** to adapt to evolving threats
+
+---
+
+## Model Details
+
+- **Model Type:** BERT-based URL Classifier
+- **Fine-Tuning Method:** LoRA (Low-Rank Adaptation)
+- **Base Model:** `bert-base-uncased`
+- **Number of Parameters:** 110M
+- **Dataset:** Kaggle Malicious URLs Dataset (~651,191 samples)
+- **Max Sequence Length:** `128`
+- **Framework:** 🤗 `transformers`, `torch`, `peft`
+
+---
+
+## How to Use
+
+You can use this model directly with 🤗 **Transformers**:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+model_name = "your-huggingface-model-name"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example URL
+url = "http://example.com/login"
+
+# Tokenize and predict
+inputs = tokenizer(url, return_tensors="pt", truncation=True, padding=True, max_length=128)
+with torch.no_grad():
+    outputs = model(**inputs)
+    prediction = torch.argmax(outputs.logits).item()
+
+# Mapping prediction to labels
+label_map = {0: "Benign", 1: "Defacement", 2: "Phishing", 3: "Malware"}
+print(f"Prediction: {label_map[prediction]}")
+```
+
+---
+
+## Training Details
+
+- **Batch Size:** `16`
+- **Epochs:** `5`
+- **Learning Rate:** `2e-5`
+- **Optimizer:** AdamW with weight decay
+- **Loss Function:** Weighted Cross-Entropy
+- **Evaluation Strategy:** Epoch-based
+- **Fine-Tuning Strategy:** LoRA applied to BERT layers
+
+---
+
+## Evaluation Results
+
+| Metric       | Value     |
+| ------------ | --------- |
+| Accuracy     | **98%**   |
+| Precision    | **0.96**  |
+| Recall       | **0.97**  |
+| **F1 Score** | **0.965** |
+
+### Category-wise Performance
+
+| Category       | Precision | Recall | F1-Score |
+| -------------- | --------- | ------ | -------- |
+| **Benign**     | 0.98      | 0.99   | 0.985    |
+| **Defacement** | 0.98      | 0.99   | 0.985    |
+| **Phishing**   | 0.93      | 0.94   | 0.935    |
+| **Malware**    | 0.95      | 0.96   | 0.955    |
+
+---
+
+## Deployment Options
+
+### 1️⃣ Streamlit Web App
+
+- Deployed on **Streamlit Cloud, AWS, or Google Cloud**.
+- Provides **real-time URL analysis** with a user-friendly interface.
+
+### 2️⃣ Browser Extension (Planned)
+
+- **Real-time scanning** of visited web pages.
+- **Dynamic threat alerts** with confidence scores.
+
+### 3️⃣ API Integration
+
+- REST API for bulk URL analysis.
+- Supports **Security Operations Centers (SOC)**.
+
+---
+
+## Limitations & Bias
+
+- **May misclassify complex phishing URLs** that mimic legitimate sites.
+- **Needs regular updates** to counter evolving threats.
+- **Potential bias** if future threats are not represented in training data.
+
+---
+
+## Training Data & Citation
+
+### Data Source
+
+Dataset sourced from **Kaggle Malicious URLs Dataset**:📌 [Dataset Link](https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset)
+
+### BibTeX Citation
+
+```
+@article{maliciousurl2025,
+  author    = {Your Name},
+  title     = {Fine-Tuned BERT for Malicious URL Detection},
+  year      = {2025},
+  journal   = {Cybersecurity AI Research},
+  url       = {https://your-research-paper-link.com}
+}
+```
+
+---
+
+## Future Work
+
+🚀 **Improvements Planned:**
+
+- **Better phishing URL detection** via adversarial training.
+- **Deploying as a real-time browser extension.**
+- **Integration with VirusTotal for enhanced threat intelligence.**
+- **Expanding detection to identify zero-day threats.**
+
+---
+
+## Uploading to Hugging Face
+
+To upload this model to **Hugging Face**, follow these steps:
+
+```bash
+pip install transformers huggingface_hub
+```
+
+```python
+from huggingface_hub import create_repo
+create_repo("your-huggingface-model-name")
+```
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from huggingface_hub import HfApi
+
+model_name = "your-huggingface-model-name"
+model = AutoModelForSequenceClassification.from_pretrained("your-local-model-directory")
+tokenizer = AutoTokenizer.from_pretrained("your-local-model-directory")
+
+# Save & Push Model
+model.save_pretrained(f"{model_name}")
+tokenizer.save_pretrained(f"{model_name}")
+
+api = HfApi()
+api.upload_folder(
+    folder_path=f"{model_name}",
+    repo_id=f"your-huggingface-username/{model_name}",
+    repo_type="model",
+)
+```
+
+---
+
+## Conclusion
+
+The **Malicious URL Detection Model** provides **state-of-the-art** accuracy for detecting **phishing, malware, and defacement threats**. It is optimized for **real-time cybersecurity applications** and **deployed using Streamlit**.
+
+✅ **Final F1-score: 0.965**✅ **Optimized for real-time detection**✅ **Ready for deployment via API & browser extension**