https://github.com/sudarshanc00/smishing

This project aims to classify text messages to detect potential smishing (SMS phishing) attacks. Using machine learning, the project provides a classifier that can differentiate between legitimate messages and smishing attempts, helping to prevent scams.
https://github.com/sudarshanc00/smishing

nltk numpy pandas python scikit-learn scipy

Last synced: about 4 hours ago
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This project aims to classify text messages to detect potential smishing (SMS phishing) attacks. Using machine learning, the project provides a classifier that can differentiate between legitimate messages and smishing attempts, helping to prevent scams.

• Host: GitHub
• URL: https://github.com/sudarshanc00/smishing
• Owner: SudarshanC00
• Created: 2024-08-25T01:19:18.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-18T06:28:19.000Z (1 day ago)
• Last Synced: 2024-11-18T07:29:01.074Z (1 day ago)
• Topics: nltk, numpy, pandas, python, scikit-learn, scipy
• Language: Python
• Homepage:
• Size: 1.12 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Smishing Detection

## Tech Stack

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white) 

![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white) 

![scikit-learn](https://img.shields.io/badge/scikit--learn-%23F7931E.svg?style=for-the-badge&logo=scikit-learn&logoColor=white) 

![Scipy](https://img.shields.io/badge/SciPy-%230C55A5.svg?style=for-the-badge&logo=scipy&logoColor=%white) 

![Streamlit](https://img.shields.io/badge/Streamlit-%23FE4B4B.svg?style=for-the-badge&logo=streamlit&logoColor=white)

## Overview

This project aims to classify text messages to detect potential smishing (SMS phishing) attacks. Using machine learning, the project provides a classifier that can differentiate between legitimate messages and smishing attempts, helping to prevent scams.

The classification model is deployed using a Streamlit app, allowing users to input text messages and get real-time classification results.

## Files

- **app.py**: Streamlit application code. This file loads the trained model and vectorizer and uses them to classify input messages as smishing or legitimate.

- **model.pkl**: Trained classification model for detecting smishing messages.

- **vectorizer.pkl**: Pre-trained vectorizer model used to transform input text data for classification.

- **nltk.txt**: File specifying NLTK dependencies (e.g., stop words) needed for text preprocessing.

- **requirements.txt**: List of dependencies required to run the project, including Streamlit and other libraries.

## Installation

1. Clone this repository:

   ```bash

   git clone https://github.com/SudarshanC00/Smishing.git

   ```

2. Navigate to the project directory:

   ```bash

   cd Smishing

   ```

3. Install dependencies:

   ```bash

   pip install -r requirements.txt

   ```

4. Download required NLTK data:

   ```bash

   python -m nltk.downloader -d /usr/local/share/nltk_data -r nltk.txt

   ```

## Usage

1. Run the Streamlit app:

   ```bash

   streamlit run app.py

   ```

2. Open the provided local URL in your browser.

3. Enter a text message to classify it as either legitimate or smishing.

## Model Details

- The model and vectorizer were pre-trained to accurately classify smishing messages.

- Both the model and vectorizer files (`model.pkl` and `vectorizer.pkl`) are loaded in `app.py` to make predictions.

## License

This project is licensed under the MIT License.