Mr. Yangbo Zhou Profile

Yangbo Zhou

at Nankai Univ

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Proceedings Article | 22 April 2022 Paper

A novel network intrusion detection model based on two-phase detection and manually labeling

Yu Zhang, Yangbo Zhou, Xiaowei Ma

Proceedings Volume 12174, 1217413 (2022) https://doi.org/10.1117/12.2628490

KEYWORDS: Computer intrusion detection, Network security, Neural networks, Machine learning

Read Abstract +

Network intrusion detection system (NIDS) is a tool that can detect various network attacks by analyzing network traffic. In recent years, traditional machine learning and deep learning methods have been widely used in NIDS. And these works achieved high detection rate and low false positive rate when identifying common attacks (such as DoS). However, the existing models performed poor in detecting rare or unseen network attacks. In this paper, we design a novel network intrusion detection model based on two-phase detection and manually labeling. This two-phase detection model (TPDM) is a multi-classification system, and it can classify network traffic into benign or specific attack type. TPDM gains the overall accuracy 98.35%, and average F1-score 99.23% on UNSW-NB15 dataset. The experiment shows that TPDM performs better than state of the art in detecting rare attacks with few training samples. Besides, it can detect unknown network attacks and then label these attacks.

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