This project involves creating a fuzzer that learns and adapts to network constraints, aimed at testing the security of a company's network through their printer connections. It's a unique approach to security testing, simulating how an attacker could potentially infiltrate a network by understanding and exploiting its weaknesses.

The goal was to join a simulated hacker challenge, with the task of infiltrating a fictional company's network through unconventional means. This involved developing a sophisticated fuzzer that not only attempts to connect to the network but learns from each attempt to improve its success rate.

• Developed a learning algorithm that analyzes both successful and unsuccessful connection attempts.
• Used the learned constraints to refine the fuzzer, making it more efficient over time.
• Implemented this system in Python, focusing on adaptability and learning from interaction with the target network.

• Learning Algorithm: Automatically identifies and adapts to network constraints.
• Security Testing: Uses a novel approach for testing network vulnerabilities through a printer connection simulation.
• Python Implementation: Built with Python for ease of use and adaptability.

This project showcases the use of learning algorithms in security testing, presenting a novel way to approach network security by simulating an attacker learning from their environment.