UFKLDA: An unsupervised feature extraction algorithm for anomaly detection under cloud environment. Issue 5 (19th June 2019)

• Record Type: Journal Article
• Authors: Wang, GuiPing; Yang, JianXi; Li, Ren
• Is Part Of: ETRI journal. Volume 41:Issue 5(2019)
• Page Start: 684
• Page End: 695
• Publication Date: 2019-06-19
• Subjects: Anomaly detection -- cloud computing -- feature extraction -- kernel method -- linear discriminant analysis; Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; Periodicals; 621.38205
• DOI: 10.4218/etrij.2018-0475
• Languages: English
• ISSNs: 1225-6463

Abstract

Abstract : In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high‐dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non‐Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C‐SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.