README.md

metadata

language: en
license: mit
library_name: transformers
tags:
  - text-classification
  - phishing-detection
  - security
  - bert
  - email-security
pipeline_tag: text-classification
base_model: bert-base-uncased
datasets:
  - custom
metrics:
  - accuracy
  - f1
widget:
  - text: >-
      URGENT: Your account will be suspended! Click here immediately to verify
      your information.
    example_title: Phishing Example
  - text: >-
      Thank you for your purchase. Your order will be shipped within 2-3
      business days.
    example_title: Legitimate Example
  - text: Verify your PayPal account now or it will be closed permanently!
    example_title: Phishing - Payment Scam
  - text: 'Your meeting reminder: Team standup at 10 AM tomorrow.'
    example_title: Legitimate - Meeting Reminder
model-index:
  - name: SecuriSense Phishing Detector
    results:
      - task:
          type: text-classification
          name: Text Classification
        metrics:
          - type: accuracy
            value: 0.9953
            name: Accuracy
          - type: f1
            value: 0.995
            name: F1 Score

SecuriSense: Phishing Email Detection Model

Model Description

SecuriSense is a fine-tuned BERT-base model specialized in detecting phishing emails with 99.54% accuracy. The model analyzes email text to classify messages as either legitimate or phishing attempts.

Developed by: Alfred Dads D. Nodado, Joshua D. Famor, Hanna Keziah T. Sato
Institution: Mapua Malayan College Mindanao
Base Model: bert-base-uncased
Language: English

Intended Use

This model is designed to:

• Classify email text as legitimate (LABEL_0) or phishing (LABEL_1)
• Assist in email security systems
• Educational purposes for cybersecurity awareness
• Integration into email filtering applications

Primary Use: Phishing detection in email security systems
Out-of-scope: Non-email text classification, multilingual detection

Training Data

The model was trained on a combined dataset of:

• Phishing Email Dataset: 18,650 samples from Kaggle
• University of Twente Validation Dataset: 1,000+ samples
• Total: 19,650+ labeled emails

The dataset includes both phishing attempts and legitimate emails with various characteristics:

• Urgency indicators
• Authority claims
• Financial requests
• Emotional manipulation patterns

Performance

Metric	Score
Accuracy	99.54%
Precision	99.73%
Recall	99.40%
F1 Score	99.56%

How to Use

Quick Start with Pipeline

from transformers import pipeline

# Load the model
classifier = pipeline(
    "text-classification",
    model="Auguzcht/securisense-phishing-detection"
)

# Classify an email
email_text = "URGENT: Your account will be suspended! Click here to verify."
result = classifier(email_text)

print(result)
# Output: [{'label': 'Phishing', 'score': 0.9987}]

Advanced Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "Auguzcht/securisense-phishing-detection"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare input
text = "Thank you for your purchase. Order #12345 will ship soon."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions).item()
    confidence = predictions[0][predicted_class].item()

# Map to label
label = model.config.id2label[predicted_class]
print(f"Label: {label}, Confidence: {confidence:.4f}")

React/JavaScript Usage

async function detectPhishing(emailText) {
  const response = await fetch(
    "https://huggingface.co/proxy/api-inference.huggingface.co/models/Auguzcht/securisense-phishing-detection",
    {
      headers: { Authorization: `Bearer ${HF_API_TOKEN}` },
      method: "POST",
      body: JSON.stringify({ inputs: emailText }),
    }
  );
  
  const result = await response.json();
  return result;
}

// Usage
const email = "URGENT: Verify your account now!";
const prediction = await detectPhishing(email);
console.log(prediction);

Label Mapping

• LABEL_0 / "Legitimate": Safe, legitimate email
• LABEL_1 / "Phishing": Phishing attempt or malicious email

Limitations

• Trained primarily on English emails
• May not detect novel phishing techniques not present in training data
• Requires clear text input (HTML should be stripped)
• Performance may vary on domain-specific jargon

Ethical Considerations

• This model is a tool to assist in security, not a replacement for human judgment
• False negatives (missed phishing) can occur - always maintain multiple security layers
• Should be used as part of comprehensive email security strategy

Citation

@misc{securisense2025,
  title={SecuriSense: Phishing Detection ML Pipeline},
  author={Nodado, Alfred Dads D. and Famor, Joshua D. and Sato, Hanna Keziah T.},
  year={2025},
  institution={Mapua Malayan College Mindanao},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/Auguzcht/securisense-phishing-detection}}
}

Contact

For questions or issues, please open an issue on the model repository or contact the authors through their institution.

License

MIT License - See LICENSE file for details