EDUCATION & TRAINING
Built a Network Traffic Classifier with Random Forest (96.8% Accuracy)
Dev.to Machine Learning
About This Tutorial
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.