Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder

• Published in: Ji suan ji ke xue Vol. 49; no. 3; pp. 144 - 151
• Main Authors: Wu, Yu-kun, Li, Wei, Ni, Min-ya, Xu, Zhi-cheng
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.03.2022; Editorial office of Computer Science
• Subjects: Anomalies; Coders; Datasets; deep auto-encoder|one-class svm|anomaly detection|hybrid model; Feature extraction; Kernel functions; Support vector machines; Training
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.210100142

Large-scale high-dimensional unbalanced data handling is a major challenge in anomaly detection.One-class support vector machine（OCSVM） is very efficient at handling unbalanced data, but it is not suitable for large-scale high-dimensional dataset.Meanwhile, the kernel function of OCSVM also has an important influence on the detection performance.An anomaly detection model combining a deep auto-encoder and a one-class support vector machine is proposed.The deep auto-encoder is not only responsible for extracting features and dimensionality reduction, but also mapping an adaptive kernel function.As a whole, the model adopts the gradient descent method to carry out joint training and realizes end-to-end training.Experiment is conducted on four public datasets and compared with other anomaly detection methods.Experimental results show that the proposed model has better performance than single-kernel or multi-kernel one-class support vector machines and other models in terms of AUC and RECALL,and the proposed mode