Deep learning for anomaly detection: A survey
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 …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
Anomaly detection in wireless sensor networks in a non-stationary environment
Anomaly detection in a WSN is an important aspect of data analysis in order to identify data
items that significantly differ from normal data. A characteristic of the data generated by a …
items that significantly differ from normal data. A characteristic of the data generated by a …
Anomalous example detection in deep learning: A survey
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection …
incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection …
A survey of anomaly detection in industrial wireless sensor networks with critical water system infrastructure as a case study
The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different
applications, including those that involve critical infrastructure, has meant that adequately …
applications, including those that involve critical infrastructure, has meant that adequately …
Contextual anomaly detection framework for big sensor data
MA Hayes, MAM Capretz - Journal of Big Data, 2015 - Springer
The ability to detect and process anomalies for Big Data in real-time is a difficult task. The
volume and velocity of the data within many systems makes it difficult for typical algorithms to …
volume and velocity of the data within many systems makes it difficult for typical algorithms to …
[PDF][PDF] Anomalous instance detection in deep learning: A survey
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques …
incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques …
Contextual anomaly detection in big sensor data
MA Hayes, MAM Capretz - 2014 IEEE International Congress …, 2014 - ieeexplore.ieee.org
Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task.
This problem is compounded as more and more sources of Big Data are generated from …
This problem is compounded as more and more sources of Big Data are generated from …
A time-series self-supervised learning approach to detection of cyber-physical attacks in water distribution systems
Water Distribution System (WDS) threats have significantly grown following the Maroochy
shire incident, as evidenced by proofed attacks on water premises. As a result, in addition to …
shire incident, as evidenced by proofed attacks on water premises. As a result, in addition to …
Web application firewall based on anomaly detection using deep learning
S Toprak, AG Yavuz - Acta Infologica, 2022 - dergipark.org.tr
Anomaly detection has been researched in different areas and application domains. The
main difficulty is to identify the outliers from the normals in case of encountering an input that …
main difficulty is to identify the outliers from the normals in case of encountering an input that …
An anomaly detection in smart cities modeled as wireless sensor network
R Jain, H Shah - 2016 International Conference on Signal and …, 2016 - ieeexplore.ieee.org
Smart city is an important application of the recent technology-Internet of Things (IoT). IoT
enables wide range of physical objects and environments to be monitored in fine detail by …
enables wide range of physical objects and environments to be monitored in fine detail by …