The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …

Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem
from adversarial or unlikely events with unknown distributions. However, the predictive …

We claim that modelling network traffic as a time series with a supervised learning approach,
using known genuine and malicious behaviour, improves intrusion detection. To …

Anomaly detection is the problem of finding patterns in data that do not conform to an a priori
expected behavior. This is related to the problem in which some samples are distant, in …

Abstract Incremental Support Vector Machines (SVM) are instrumental in practical
applications of online learning. This work focuses on the design and analysis of efficient …

Wearable devices use sensors to evaluate physiological parameters, such as the heart rate,
pulse rate, number of steps taken, body fat and diet. The continuous monitoring of …

Detection methods based on n-gram models have been widely studied for the identification
of attacks and malicious software. These methods usually build on one of two learning …

Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide
variety of applications such as earth science, traffic monitoring, fraud and disease outbreak …

The anomaly detection methods are receiving growing attention in the intrusion detection
community. The two main reasons for this are their ability to handle large volumes of …