Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2017, Article ID 8501683, 9 pages
https://doi.org/10.1155/2017/8501683
Research Article

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

Information and Telecommunication Engineering College, Beijing University of Posts and Telecommunications, Beijing, China

Correspondence should be addressed to Hongchao Song; moc.qq@hch_gnos

Received 14 September 2017; Accepted 26 October 2017; Published 15 November 2017

Academic Editor: Pedro Antonio Gutierrez

Copyright © 2017 Hongchao Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble -nearest neighbor graphs- (-NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.