Deep Learning-Based Network Intrusion Detection and Prevention System
Year: 2026
Type: article
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%.
Author: Huang Nana
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7160607231
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