File size: 1,862 Bytes
bcbe85a 61f0b24 bcbe85a 9d8cc81 bcbe85a 9e9d741 bcbe85a 08d3ea1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
---
title: Malicious Email & Url Detector
emoji: 📊
colorFrom: red
colorTo: yellow
sdk: streamlit
sdk_version: 1.43.2
app_file: app.py
pinned: false
short_description: A web app for detecting malicious Email and URL
---
# Malicious Email & URL Detector
This is the first version of **Malicious-URL-Detector**, a web application built using Streamlit that leverages a fine-tuned deep learning model to detect malicious emails and URLs. The application analyzes input text—whether it’s the content of an email or a URL string—and classifies it as either malicious (e.g., phishing or malware) or benign.
## How It Works
- **Model Integration:**
The app uses a model fine-tuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) for text classification. The model has been trained on a curated dataset comprising phishing, malware, and legitimate examples, enabling it to recognize suspicious patterns and linguistic cues.
- **User Interface:**
Built with Streamlit, the web app offers a simple and intuitive interface where users can paste the content of an email or a URL. Upon submission, the model processes the input and returns a prediction indicating whether the text is malicious or benign, along with a confidence score.
- **Real-Time Detection:**
Designed for real-time threat detection, the application helps organizations and individual users quickly identify potentially harmful links before they are accessed, thereby contributing to enhanced cybersecurity defenses.
## Getting Started
To run the application locally or deploy it on Hugging Face Spaces, follow these steps:
1. **Clone the Repository:**
Clone this repository to your local machine.
```bash
git clone https://huggingface.co/spaces/your-username/Malicious-URL-Detector
cd Malicious-URL-Detector
|