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Recent Progress of Anomaly Detection

Complexity, 2019
Anomaly analysis is of great interest to diverse fields, including data mining and machine learning, and plays a critical role in a wide range of applications, such as medical health, credit card fraud, and intrusion detection.
Xiaodan Xu, Huawen Liu, Minghai Yao
doaj   +1 more source

Anomaly detection of multivariate time series for industrial machinery [PDF]

openIn industrial contexts, anomaly detection is crucial for identifying deviations from normal operating conditions, ensuring proactive maintenance, minimising downtime, and optimising the reliability and efficiency of industrial processes.
BEE, NICOLA
core  

Rating the Significance of Detected Network Events

, 2014
Existing anomaly detection systems do not reliably produce accurate severity ratings for detected network events, which results in network operators wasting a large amount of time and effort in investigating false alarms.
Mungro, Meenakshee
core  

A systematic survey: role of deep learning-based image anomaly detection in industrial inspection contexts

Frontiers in Robotics and AI
Industrial automation is rapidly evolving, encompassing tasks from initial assembly to final product quality inspection. Accurate anomaly detection is crucial for ensuring the reliability and robustness of automated systems.
Vinita Shukla, Amit Shukla, Surya Prakash S. K., Shraddha Shukla   +3 more
doaj   +1 more source

Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis [PDF]

, 2012
Systematic network monitoring can be the cornerstone for the dependable operation of safety-critical distributed systems. In this paper, we present our vision for informed anomaly detection through network monitoring and resilience measurements to ...
Johnson, Christopher W., Pezaros, Dimitrios P., White, Kyel .J.S.   +2 more
core  

Fault detection for binary sensors in smart home environments

, 2015
Experiments in assisted living confirm that such systems can provide context-aware services that enable occupants to remain active and independent. They also demonstrate that abnormal sensor events hamper the correct identification of critical (and ...
Ye, Juan, Stevenson, Graeme, Graeme Stevenson, Simon Dobson, Juan Ye, Dobson, Simon   +5 more
core   +1 more source

Models and Methods for Automated Background Density Estimation in Hyperspectral Anomaly Detection

, 2012
Detecting targets with unknown spectral signatures in hyperspectral imagery has been proven to be a topic of great interest in several applications. Because no knowledge about the targets of interest is assumed, this task is performed by searching the ...
VERACINI, TIZIANA
core  

Transfer learning applications for anomaly detection in wind turbines

Anomaly detection in wind turbines typically involves using normal behaviour models to detect faults early. However, training autoencoder models for each turbine is time-consuming and resource intensive. Thus, transfer learning becomes essential for wind
Roelofs, Cyriana Maria Antonia, Faulstich, Stefan, Roelofs, Cyriana M. A., Gück, Christian   +3 more
core   +1 more source

Enhancing Anomaly Detection: Integrating Human Feedback Through Active Learning

reservedIn the realm of data-intensive fields, obtaining accurate labels becomes increasingly challenging, particularly in Anomaly Detection, where anomalies are context-dependent and difficult to define.
BAZ RADWAN, FAIROUZ
core  

pyCLAD: The universal framework for continual lifelong anomaly detection

SoftwareX
Anomaly detection is a recognized problem with high significance and impact in many real-world settings. Continual anomaly detection is an emerging paradigm that allows for the design of anomaly detection methods capable of adapting to new challenges in ...
Kamil Faber, Bartlomiej Sniezynski, Nathalie Japkowicz, Roberto Corizzo   +3 more
doaj   +1 more source