🛡️ VeloxGuard AI: Real-Time Phishing Intelligence

VeloxGuard AI is a next-generation cybersecurity tool built to detect malicious URLs using Machine Learning. By analyzing structural patterns in URLs, it identifies phishing threats in real-time before a user clicks.

🚀 Live Demo

[Link to your Streamlit Cloud URL here]

✨ Key Features

• Behavioral Analysis: Does not rely on static blacklists; it analyzes URL "behavior" (length, IP usage, sub-domains).
• AI-Powered: Utilizes a Random Forest Classifier trained on 11,000+ samples.
• Confidence Scoring: Provides a percentage-based threat probability for "Explainable AI."
• Cloud-Native: Fully integrated with GitHub and ready for one-click deployment on Streamlit Cloud.

📊 Dataset & Methodology

This project utilizes the UCI Phishing Website Dataset. The model is trained on 30 features, focusing on:

1. IP Address Presence: Detecting hidden server identities.
2. URL Length: Flagging obfuscated or long-tail links.
3. Symbol Injection: Identifying @ symbols used to mask destinations.
4. Sub-domain Multiplicity: Detecting "layered" URLs common in phishing.

🛠️ Tech Stack

• Language: Python 3.10+
• ML Library: Scikit-Learn (Random Forest)
• Data Handling: Pandas & NumPy
• Web Framework: Streamlit
• Hosting: GitHub + Streamlit Community Cloud

⚙️ Installation & Local Setup

1. Clone the repository:

Bash

pip install -r requirements.txt Run the Application:

Bash

streamlit run app.py 📸 Interface Preview The dashboard features a sleek dark-mode UI with sidebar diagnostics, real-time progress indicators, and safety metrics.

🎓 Academic Credit Objective: To bridge the gap between static firewall rules and intelligent heuristic threat detection.

© 2025 VeloxGuard AI | Secure the Web

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How to add this to your GitHub:

1. Open your project folder.
2. Create a new file named README.md (make sure it ends in .md).
3. Paste the code above into it.
4. Save and upload it to your GitHub repository along with app.py, dataset.csv, and `requirements.txt