File size: 2,357 Bytes
bcbe85a 25d2a02 bcbe85a 9d8cc81 bcbe85a 25d2a02 bcbe85a 08d3ea1 25d2a02 08d3ea1 25d2a02 08d3ea1 25d2a02 08d3ea1 25d2a02 08d3ea1 25d2a02 08d3ea1 25d2a02 08d3ea1 25d2a02 08d3ea1 25d2a02 08d3ea1 25d2a02 f5b0f6a 25d2a02 f5b0f6a 25d2a02 f5b0f6a 25d2a02 f5b0f6a 25d2a02 f5b0f6a 25d2a02 f5b0f6a 25d2a02 f5b0f6a |
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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
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 emails and URLs
---
# Malicious Email & URL Detector
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 [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) (or your chosen model) 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-URL-Detector
cd Malicious-URL-Detector
2. **Install Dependencies**
pip install -r requirements.txt
3. **Run the App**
streamlit run app.py
4. **Use It**
Paste an email’s content or a URL into the text box.
Click Analyze to see the classification results.
5. **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 |