# PhilBERT: Phishing Detection with DistilBERT

PhilBERT is a **fine-tuned DistilBERT model** optimized for detecting phishing threats across multiple communication channels, including **emails, SMS, URLs, and websites**. It is trained on a diverse dataset sourced from **Kaggle, Mendeley, Phishing.Database, and Bancolombia**, ensuring high adaptability and real-world applicability.

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## Key Features

- **Multi-Channel Detection** – Analyzes text, URLs, and web content to detect phishing patterns.
- **Fine-Tuned on Real-World Data** – Includes recent **three months of financial institution data (Bancolombia)**.
- **Lightweight & Efficient** – Based on **DistilBERT**, providing high performance with reduced computational costs.
- **High Accuracy** – Achieves **85.22% precision, 93.81% recall, and 88.77% accuracy** on unseen data.
- **Self-Adaptive Learning** – Continuously evolves using real-time phishing simulations generated with **GPT-4o**.
- **Scalability** – Designed to support **7,000–25,000 simultaneous users** in production environments.

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## Model Architecture

PhilBERT leverages **DistilBERT**, a distilled version of **BERT**, maintaining the same architecture but with **40% fewer parameters**, making it lightweight while preserving high accuracy. The **final model** includes:

- **Tokenizer**: Trained to recognize phishing-specific patterns (URLs, obfuscation, domain misspellings).
- **Custom Classifier**: A **fully connected dense layer** added for binary classification (phishing vs. benign).
- **Risk Scoring Mechanism**: A **weighted confidence score** applied to enhance detection reliability.

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## Data Preprocessing

Before fine-tuning, the dataset underwent **extensive preprocessing** to ensure balance and quality:

- **Duplicate Removal & Balancing**: Maintained a **near 50-50 phishing-to-benign ratio** to prevent model bias.
- **Feature Extraction**: Applied to **URLs, HTML, email bodies, and SMS content** to enrich input representations.
- **Dataset Split**: Final dataset included:
  - **427,028 benign URLs** & **381,014 phishing URLs**
  - **17,536 unique email samples**
  - **5,949 SMS samples**
  - **Web entries filtered for efficiency** (removing entries >100KB).
- **Export Format**: Data transformed and stored in **JSON for efficient training**.

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## Training & Evaluation

PhilBERT was fine-tuned on **multi-modal phishing datasets** using **transfer learning**, achieving:

| **Metric**          | **Value**  |
|---------------------|------------|
| Accuracy           | **88.77%**  |
| Precision         | **85.22%**  |
| Recall            | **93.81%**  |
| F1-Score         | **89.31%**  |
| Evaluation Runtime | **130.46s** |
| Samples/sec       | **58.701**  |

- **False Positive Reduction**: Multi-layered filtering minimized false positives while maintaining **high recall**.
- **Scalability**: Successfully stress-tested for **up to 25,000 simultaneous users**.
- **Compliance**: Meets **ISO 27001** and **GDPR standards** for security and privacy.

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## Usage

### Installation

```bash
pip install transformers torch
```

### Inference

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "your_username/PhilBERT"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "Click this link to update your bank details: http://fakebank.com"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

print(f"Phishing probability: {predictions[0][1].item():.4f}")
```

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## License

This model is **proprietary** and protected under a **custom license**. Please refer to the **[LICENSE](LICENSE)** file for terms of use.

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