[HTML][HTML] Deep industrial image anomaly detection: A survey
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 …
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
Network anomaly detection: methods, systems and tools
MH Bhuyan, DK Bhattacharyya… - … surveys & tutorials, 2013 - ieeexplore.ieee.org
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 …
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …
Toward supervised anomaly detection
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem
from adversarial or unlikely events with unknown distributions. However, the predictive …
from adversarial or unlikely events with unknown distributions. However, the predictive …
Applying long short-term memory recurrent neural networks to intrusion detection
RC Staudemeyer - South African Computer Journal, 2015 - journals.co.za
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 …
using known genuine and malicious behaviour, improves intrusion detection. To …
[HTML][HTML] Anomaly detection based on sensor data in petroleum industry applications
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 …
expected behavior. This is related to the problem in which some samples are distant, in …
[PDF][PDF] Incremental support vector learning: Analysis, implementation and applications.
Abstract Incremental Support Vector Machines (SVM) are instrumental in practical
applications of online learning. This work focuses on the design and analysis of efficient …
applications of online learning. This work focuses on the design and analysis of efficient …
[HTML][HTML] Anomaly detection framework for wearables data: a perspective review on data concepts, data analysis algorithms and prospects
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 …
pulse rate, number of steps taken, body fat and diet. The continuous monitoring of …
A close look on n-grams in intrusion detection: anomaly detection vs. classification
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 …
of attacks and malicious software. These methods usually build on one of two learning …
Unsupervised anomaly detection in multivariate spatio-temporal data using deep learning: early detection of COVID-19 outbreak in Italy
Y Karadayi, MN Aydin, AS Öǧrencí - Ieee Access, 2020 - ieeexplore.ieee.org
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 …
variety of applications such as earth science, traffic monitoring, fraud and disease outbreak …
Intrusion detection in unlabeled data with quarter-sphere support vector machines
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 …
community. The two main reasons for this are their ability to handle large volumes of …