#AI-Enhanced Honeypot System
Overview:
This project presents an AI-Enhanced Honeypot System designed to simulate vulnerable web services to detect and analyze malicious network traffic. The system integrates a Flask-based web application with a dynamic TensorFlow neural network to classify incoming requests as normal or potentially malicious. By leveraging real-time data processing and continuous learning, the system aims to improve its detection capabilities over time, providing valuable insights into security threats.
Features:
Flask Web Application: Acts as the honeypot, simulating vulnerable endpoints to attract malicious interactions.
Dynamic Neural Network: Utilizes TensorFlow to create a neural network model that classifies request behaviors.
Real-Time Data Preprocessing: Converts incoming HTTP requests into a numerical format suitable for the neural network.
Continuous Learning: Adapts to new threats by updating the model’s knowledge base with every detected interaction.
Anomaly Detection: Identifies patterns deviating from normal behavior, flagging potentially malicious activities. System Components
Flask Application: Serves as the interface for incoming HTTP requests, simulating endpoints that appear vulnerable to attackers.
Preprocessing Function: Processes incoming data to fit the neural network’s expected input structure, ensuring efficient data handling and analysis.
Neural Network Model: Built with TensorFlow, the model predicts whether incoming requests are normal or anomalous based on learned patterns.
Model Update Mechanism: Allows the system to learn from new data continuously, employing online learning techniques to refine its predictive capabilities.
Response Mechanism: Depending on the prediction, the system can log details, trigger alerts, or take predefined actions to mitigate potential threats.
Installation To set up the AI-Enhanced Honeypot System, follow these steps:
Clone the Repository:
bash Copy code git clone https://github.com/yourgithub/ai-enhanced-honeypot.git Install Dependencies:
Copy code pip install -r requirements.txt Initialize the Neural Network Model:
Ensure TensorFlow is properly installed and configured. Run the script to create and initially train your model with baseline data. Start the Flask Application:
Copy code python app.py Usage Upon launching the Flask application, the system starts simulating the vulnerable web service. It will begin to classify incoming requests in real time, learning from each interaction to enhance its detection capabilities.
Contribution Contributions to the AI-Enhanced Honeypot System are welcome. Whether it’s feature enhancement, bug fixes, or improvements to the AI model, your input is valuable. Please follow the standard GitHub pull request process to submit your contributions.
Ethical Use and Legal Compliance This system is designed for educational and research purposes only. Users are responsible for ensuring that their deployment of the AI-Enhanced Honeypot System complies with all relevant laws and ethical guidelines, especially regarding data collection and privacy.
License This project is licensed under the MIT License – see the LICENSE file for details.