Built a Network Traffic Classifier with Random Forest (96.8% Accuracy)

#cybersecurity #ai #machinelearning #python

I recently completed a cybersecurity + machine learning project where I trained a Random Forest model to classify network traffic into multiple attack categories using the NSL-KDD dataset.

The classifier can detect:

DoS attacks
Probe/reconnaissance traffic
R2L brute-force attempts
U2R privilege escalation
Normal traffic
Stack
Python
Scikit-learn
FastAPI
Pandas / NumPy
Results
96.8% Accuracy
<1ms inference time
Production-ready model packaging

I also wrote a detailed Medium article covering:

Dataset preprocessing
Feature selection
Model training
API integration
Real-world deployment considerations
Challenges working with synthetic vs real traffic data

Would appreciate feedback from the community, especially from people working in:

Cybersecurity
ML Engineering
Intrusion Detection
Backend Systems

Article: https://medium.com/@gulrezqayyum/building-a-production-ready-network-traffic-classifier-with-random-forest-and-nsl-kdd-b9559346e197

GitHub:
https://github.com/GulrezQayyum

LinkedIn:
https://www.linkedin.com/in/gulrez-qayyum-999345322/