Fast Kernel-based Method for Anomaly Detection

Anh Le, Trung Le, Khanh Nguyen, Van Nguyen, Thai Le, Dat TRAN

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

Anomaly detection (AD) involves detecting abnormality from normality and has a wide spectrum of applications in reality. Kernel-based methods for AD have been proven robust with diverse data distributions and offering good generalization ability. Stochastic gradient descent (SGD) method has recently emerged as a promising framework to devise ultra-fast learning methods. In this paper, we conjoin the advantages of Kernel-based method and SGD-based method to propose fast learning methods for anomaly detection. We validate the proposed methods on 8 benchmark datasets in UCI repository and KDD cup 1999 dataset. The experimental results show that the proposed methods offer a comparable one-class classification accuracy while simultaneously achieving a significantly computational speed-up
Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
EditorsHussein A. Abbass Huanhuan Chen
Place of PublicationUnited States of America
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3211-3217
Number of pages7
ISBN (Electronic)9781509006205
ISBN (Print)9781509006212
DOIs
Publication statusPublished - 2016
Event2016 International Joint Conference on Neural Networks - Vancouver, Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Conference

Conference2016 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

Cite this

Le, A., Le, T., Nguyen, K., Nguyen, V., Le, T., & TRAN, D. (2016). Fast Kernel-based Method for Anomaly Detection. In H. A. A. H. Chen (Ed.), 2016 International Joint Conference on Neural Networks, IJCNN 2016 (pp. 3211-3217). (Proceedings of the International Joint Conference on Neural Networks; Vol. 2016-October). United States of America: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ijcnn.2016.7727609