Machine Learning for Cybersecurity Threat Detection

I'm looking for a professional with strong expertise in machine learning and cybersecurity to help develop an attack detection system that can be integrated into any simulated network environment. The system should be capable of detecting port scanning and DDoS attacks by analyzing network traffic logs.

Requirements:
The model must be adaptable for integration with various network simulation platforms (e.g., GNS3, SDN environments).
It should focus on detecting port scanning and DDoS attacks effectively.
A supervised learning approach is preferred, leveraging labeled network traffic data for training.
Ideal Skills:
Expertise in machine learning, particularly supervised learning techniques.
Strong background in cybersecurity, with hands-on experience in detecting port scanning and DDoS attacks.
Proficiency in network traffic analysis and working with network logs.
Experience integrating machine learning models into network simulation environments.
Your task will be to design a reliable and efficient attack detection system that can seamlessly integrate with network simulations and enhance cybersecurity defenses.

Please provide examples of similar projects you've worked on and how you ensured the model's adaptability to different network environments.