Built a Network Traffic Classifier with Random Forest (96.8% Accuracy)
Gulrez Qayyum has developed a network traffic classifier using a Random Forest model, achieving an accuracy of 96.8%. The classifier is capable of identifying various types of network attacks and normal traffic. Qayyum shared insights on the project, including dataset preprocessing and real-world deployment considerations.
- ▪The classifier can detect DoS attacks, probe/reconnaissance traffic, R2L brute-force attempts, U2R privilege escalation, and normal traffic.
- ▪The project utilized the NSL-KDD dataset and achieved a production-ready model with less than 1ms inference time.
- ▪Qayyum provided a detailed article covering aspects such as feature selection, model training, and API integration.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3896088) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Gulrez Qayyum Posted on May 17 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.
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