Real-Time Network Intrusion Prevention System Based on Machine Learning)

Abstract

This project presents the design and implementation of a Real-Time Network Intrusion Prevention System (RT-NIPS) based on a machine learning methodology. The system addresses the fundamental challenge in network security: achieving both high detection accuracy and real-time processing speed simultaneously. The proposed system employs a two-level classifier architecture. Level 1 performs fast classification using Decision Tree (DT) on the first packet of each flow. Level 2 performs fine-grained analysis on low-confidence flows using Random Forest (RF) with full flow statistics. The system was trained and evaluated on two benchmark datasets: UNSW-NB15 and CICIDS2017. Key results demonstrate that the two-level approach successfully balances speed and accuracy.