What question did this study set out to answer?

This research aims to enhance network intrusion detection and prevention through advanced feature representation and real-time response mechanisms.

May 10, 2026Open Access

Deep Learning-Based Network Intrusion Detection and Prevention System

Key Points

This research aims to enhance network intrusion detection and prevention through advanced feature representation and real-time response mechanisms.
Developed an intelligent system by fusing NetFlow and payload features.
Implemented a three-level detection engine using deep autoencoder, Transformer, and GNN.
Conducted experiments on a mixed dataset from CIC-IDS2018 and UNSW-NB15.
Achieved a detection rate of 98.7% and a false positive rate of 0.86%.
Obtained an average detection rate of 87.04% for unknown attacks.
Real-time interception success reached 99%.

Abstract

Current network intrusion detection systems struggle with feature representation, unknown attack detection, and coordinated response. This paper proposes an intelligent system that fuses NetFlow and payload features, employs a three-level detection engine (deep autoencoder, Transformer, GNN), and integrates with softwaredefined networking for real-time mitigation and adaptive feedback-driven model improvement. Experiments on a mixed dataset combining the CIC-IDS2018 and UNSW-NB15 show a detection rate of 98.7%, a false positive rate of 0.86%, and an average detection rate of 87.04% for unknown attacks, with real-time interception success reaching 99%.

Deep Learning-Based Network Intrusion Detection and Prevention System

Key Points

Abstract

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