Hey everyone!

I'm in the last year of my BS in Cyber Security program and my classmate and I are doing our final year project on:

“ML-Based Real-Time Network Monitoring System”

We want to build a system to help network administrators monitor LAN traffic in real-time and detect all types of anomalies using machine learning. Our goal is to create a practical and impactful tool that could genuinely improve network security not just a theoretical project.

• Successfully defended our project proposal

• Selected modern datasets like:

  • CESNET-TimeSeries24 (real-world ISP traffic)

  • Gotham 2025 (IoT attacks)

  • 5G-NIDD (5G network threats)

• Planning to use tools like Python, Scikit-learn

• Targeting real-time anomaly detection using both supervised and unsupervised ML

• Suggestions for system architecture (real-time detection pipeline)

• Best practices for feature extraction from network traffic

• Ideas for visualizing alerts and traffic patterns

• General feedback on how to make this a robust and impactful product

• Need suggestions on finalize datasets

• Need suggestions on ML models

If you’ve worked on similar projects or have insights, tools, or papers to recommend, we’d love to hear from you. Also open to collaboration or mentorship!

Thanks in advance!