README.md

metadata

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.

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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.

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How It Works

1. Model
   A fine-tuned variant of 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.

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Quickstart

1. Clone the Repository

   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