Computer Science ›› 2022, Vol. 49 ›› Issue (3): 144-151.doi: 10.11896/jsjkx.210100142

• Database & Big Data & Data Science • Previous Articles     Next Articles

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

WU Yu-kun, LI Wei, NI Min-ya, XU Zhi-cheng   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2021-01-18 Revised:2021-05-01 Online:2022-03-15 Published:2022-03-15
  • About author:WU Yu-kun,born in 1980,Ph.D student,is a member of China Compu-ter Federation.His main research in-terests include machine learning and big data.
    LI Wei,born in 1958,Ph.D,professor.His main research interests include big data,block chain,IOT,and smart city development.
  • Supported by:
    National Natural Science Foundation of China(61502422,61972056),Natural Science Foundation of Zhejiang Province,China(LY18F020028),Public Welfare Project of Zhejiang Science and Technology Department(2017C33108) and General Research Project of Zhejiang Provincial Department of Education(Y202044619).

Abstract: 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 model is robust at different anomaly rate and has great advantages in time complexity.

Key words: Anomaly detection, Deep auto-encoder, Hybrid model, One-class SVM

CLC Number: 

  • TP391
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