A Review of Network Anomaly Detection Based on Semi-Supervised Learning
The acquisition of network traffic data is relatively easy,while marking the traffic data is comparatively challenging.Semi-supervised learning utilizes a small amount of labeled data and a large amount of unlabeled data for training,reducing the demand for labeled data and effectively adapting to anomaly detection in massive network traffic data.This paper conducted an in-depth investigation into the field of semi-supervised network anomaly detection in recent years.Firstly,it introduced some basic concepts and thoroughly analyzes the necessity of using semi-supervised learning strategies in network anomaly detection.Then,from the perspectives of semi-supervised machine learning,semi-supervised deep learning,and the combination of semi-supervised learning with other paradigms,it analyzed and compared the recent literature on semi-supervised network anomaly detection and summarized the findings.Finally,the current status and future prospects of the field of semi-supervised network anomaly detection were analyzed.
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