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---
title: Malicious Email & URL Detector v2
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 emails and URLs
---
# Malicious Email & URL Detector v2
A lightweight **Streamlit** web application that utilizes a fine-tuned deep learning model to detect malicious content in emails and URLs. The app helps individuals and organizations identify threats such as **phishing** and **malware** before any harm can occur.
---
## Key Features
- **Real-Time Detection**
Quickly classifies emails or URLs as **malicious** or **benign** using a fine-tuned transformer model.
- **User-Friendly Interface**
Paste the email text or URL, then click a button—no advanced knowledge required.
- **Lightweight & Fast**
Built on Streamlit for a snappy, interactive experience.
---
## How It Works
1. **Model**
A fine-tuned variant of [distilroberta-base](https://huggingface.co/distilroberta-base) trained on a curated dataset of phishing, malware, and legitimate examples.
2. **Input**
Users provide either an email’s textual content or a single URL. The app normalizes and processes the input.
3. **Inference**
The model returns a **label** (malicious/benign) and a **confidence score**, enabling quick decisions on blocking or flagging potential threats.
---
## Quickstart
1. **Clone the Repository**
```bash
git clone https://huggingface.co/spaces/your-username/Malicious-Email-and-URL-Detector-v2
cd Malicious-Email-and-URL-Detector-v2
2. **Install Dependencies**
pip install -r requirements.txt
3. **Run the App**
streamlit run app.py
4. **Use It**
Step 1: Paste the email content or URL into the input box.
Step 2: Click Analyze.
Step 3: View the output displaying the classification (malicious or benign) and the confidence score.
6. **Example**
Input:
"Hello, your account has been locked. Please verify at http://suspicious-link.com"
Output:
Malicious (Confidence: 0.95)
## Limitations
Limitations
False Positives/Negatives: No model is perfect. Always combine with other security measures.
Dataset Bias: Performance depends on how well the training data represents real-world threats.
Evolving Threats: Regular updates are recommended to keep pace with new phishing or malware tactics.
## Contact
Author: Eason Liu |