Summary
Asia-Pacific Network Operations and Management Symposium
2022
Session Number:TS4
Session:
Number:TS4-03
Probability Correlation Learning for Anomaly Detection based on Distribution-Constrained Autoencoder
Jihua Wu, Lei Zhang, Cong Liu, Qi Qi, Jingyu Wang, Tong Xu, Jianxin Liao,
pp.-
Publication Date:2022/09/28
Online ISSN:2188-5079
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Summary:
Network anomaly detection provides a reliable and stable service to detect faults and prevent security attacks effectively. However, existing detection methods still encounter many challenges. The supervised learning method is unsuitable because the anomaly samples are seriously sparse and hard to label. Unsupervised learning, as a promising method, is widely used while the discriminative features are ignored when reconstructing from the normal feature space. This paper proposes a novel probability correlation learning based on autoencoder called PCDetect, a semi-supervised learning method. Since we assumed the anomaly samples deviate from the distribution of normal samples, approximating the distribution of original data is proposed as an efficient preprocessing methodology to capture the discriminative features.