UFKLDA: An unsupervised feature extraction algorithm for anomaly detection under cloud environment

作者：Guiping Wang, Jianxi Yang, Ren Li · 发表于：ETRI Journal · 年份：2019 · DOI：10.4218/etrij.2018-0475 · 被引用次数：19 · 研究领域：Network Security and Intrusion Detection、Anomaly Detection Techniques and Applications、Advanced Malware Detection Techniques

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.