Phishmail

🚀 PhishMail - BERT Model for Phishing Detection

This repository presents a fine - tuned BERT model tailored for detecting phishing emails. The model analyzes the body text of emails to classify them as either phishing or legitimate, thus enhancing email security.

🚀 Quick Start

Prerequisites

You need to install the necessary libraries using the following command:

!pip install transformers torch 

Loading the Model

from transformers import BertForSequenceClassification, BertTokenizer
import torch

# Specify the Hugging Face model repository name
model_name = 'jagan-raj/PhishMail'

# Load the fine-tuned BERT model for phishing detection
model = BertForSequenceClassification.from_pretrained(model_name)

# Load the corresponding tokenizer for the fine-tuned model
tokenizer = BertTokenizer.from_pretrained(model_name)

# Set the model to evaluation mode for inference
model.eval()

Making Predictions

# Input the email text for classification
email_text = "Your email content here"

# Tokenize and preprocess the input text
# Converts the email text into token IDs, applies truncation/padding, and creates a tensor
inputs = tokenizer(
    email_text, 
    return_tensors="pt",        # Output tensors in PyTorch format
    truncation=True,            # Truncate the text if it exceeds the max_length
    padding='max_length'        # Pad the text to the maximum sequence length
)

# Make a prediction using the model
with torch.no_grad():           # Disable gradient calculations for faster inference
    outputs = model(**inputs)   # Get model outputs
    logits = outputs.logits     # Extract raw prediction scores (logits)
    predictions = torch.argmax(logits, dim=-1)  # Determine the predicted class (0 or 1)

# Interpret the prediction result
# Map the prediction to its corresponding label: 1 for "Phishing", 0 for "Legitimate"
result = "This is a phishing email." if predictions.item() == 1 else "This is a legitimate email."

# Print the prediction result
print(f"Prediction: {result}")

✨ Features

• Accurate Detection: The model can accurately classify emails as phishing or legitimate by analyzing the email body text.
• Contextual Understanding: It leverages the power of BERT to understand the context of the email text, enabling it to detect subtle phishing cues.

📦 Installation

Use the following command to install the required dependencies:

!pip install transformers torch 

💻 Usage Examples

Basic Usage

from transformers import BertForSequenceClassification, BertTokenizer
import torch

model_name = 'jagan-raj/PhishMail'
model = BertForSequenceClassification.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)
model.eval()

email_text = "Your email content here"
inputs = tokenizer(email_text, return_tensors="pt", truncation=True, padding='max_length')
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-1)
result = "This is a phishing email." if predictions.item() == 1 else "This is a legitimate email."
print(f"Prediction: {result}")

📚 Documentation

Model Details

Evaluation

TrainOutput(global_step=6297, training_loss=0.07093968526965307, metrics={'train_runtime': 5545.442, 'train_samples_per_second': 9.08, 'train_steps_per_second': 1.136, 'total_flos': 1.32489571926528e+16, 'train_loss': 0.07093968526965307, 'epoch': 3.0})

📄 License

The model is free for all to use.

👨‍💻 Author

• Jagan Raj
• LinkedIn Profile