CyberLock: AI-Powered Cyber Threat Detection

🔒 Overview

CyberLock is a comprehensive cybersecurity tool designed to detect and analyze various cyber threats, including phishing emails, suspicious URLs, and scam patterns. It provides an interactive interface with real-time analysis and educational insights to help users understand and prevent online attacks. The project leverages machine learning, pattern recognition, and web scraping techniques to enhance security awareness and protection.

🚀 Features

• Email Attack Detection: Identifies phishing, scam, and malware-laden emails using keyword analysis and pattern matching.
• URL Analyzer: Evaluates the legitimacy of URLs to detect malicious websites.
• Cyber Attack Encyclopedia: Provides detailed information on various cyber attack types, including their mechanisms, consequences, and prevention methods.
• Scam Pattern Detection: Recognizes common scam messages and fraud patterns based on predefined keywords and hashed scam indicators.
• Interactive Dashboard: User-friendly GUI for easy navigation and real-time threat analysis.

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/ahamed-ali-git/CyberLock-AI-Powered-Cyber-Threat-Detection.git
   cd cyberlock

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the application:

   python Main.py

📖 How It Works

• Email Attack Detection: Extracts text from emails, checks for predefined scam patterns, and classifies them as spam or ham.
• URL Analysis: Uses web scraping and dataset comparison to verify whether a URL is malicious or legitimate.
• Cyber Attack Encyclopedia: Displays categorized attack types with a dynamic UI and prevention tips.
• Scam Pattern Recognition: Compares text messages against a hashed scam pattern database for fraud detection.

📌 Dependencies

• Python 3.x
• CustomTkinter
• Matplotlib
• Pandas
• BeautifulSoup4
• Requests
• Tkinter

🎯 Future Enhancements

• Integration with AI-based models for advanced threat detection
• Real-time phishing email detection with ML algorithms
• Cloud-based reporting and alert system

🤝 Contributing

Feel free to fork the repository and submit pull requests. For major changes, open an issue first to discuss the proposed modifications.

🏆 Credits

Developed by Ahamed Ali Z

📧 Contact: ahamedaliz2004@gmail.com
🌐 GitHub: ahamed-ali-git