Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Reconstruction-based methods play an important role in unsupervised anomaly detection in
images. Ideally, we expect a perfect reconstruction for normal samples and poor …

Deep approaches to anomaly detection have recently shown promising results over shallow
methods on large and complex datasets. Typically anomaly detection is treated as an …

With the recent advances in deep neural networks, anomaly detection in multimedia has
received much attention in the computer vision community. While reconstruction-based …

While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …

This paper presents a novel feature learning model for cyber security tasks. We propose to
use Auto-encoders (AEs), as a generative model, to learn latent representation of different …

Many well-established anomaly detection methods use the distance of a sample to those in
its local neighbourhood: so-calledlocal outlier methods', such as LOF and DBSCAN. They …

Recently, anomaly detection and localization in multimedia data have received significant
attention among the machine learning community. In real-world applications such as …