Aaqib099/distilbert-phishing-detection

DistilBERT Phishing Email Detection

Model Description

This model detects phishing emails using a fine-tuned DistilBERT transformer model.

• Base model: distilbert-base-uncased
• Architecture: Encoder-only
• Task: Binary text classification
• Classes: Safe Email (0), Phishing Email (1)

Dataset

Dataset: Phishing Email Dataset

After cleaning, 17,537 email samples were used.

• Training: 14,029
• Validation: 1,754
• Testing: 1,754

Training

• Epochs: 2
• Learning rate: 2e-5
• Maximum sequence length: 256 tokens
• Framework: Hugging Face Transformers
• Hardware: NVIDIA Tesla T4 GPU

Test Results

Metric	Score
Accuracy	98.86%
Precision	98.33%
Recall	98.63%
F1-score	98.48%

Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="Aaqib099/distilbert-phishing-detection"
)

result = classifier(
    "Urgent! Verify your account by clicking this link."
)

print(result)

Limitations

• Trained on a specific phishing email dataset.
• Results may differ on real-world messages.
• Input is truncated to 256 tokens during training.
• Predictions should not be treated as guaranteed security decisions.

Project

Fine-tuning and comparison of encoder-only and decoder-only transformer architectures for phishing detection.