Construction of health indicators for condition monitoring of rotating machinery: A review of the research
The condition monitoring (CM) of rotating machinery (RM) is an essential operation for
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …
A review of time-series anomaly detection techniques: A step to future perspectives
Anomaly detection is a significant problem that has been studied in a broader spectrum of
research areas due to its diverse applications in different domains. Despite the usage of …
research areas due to its diverse applications in different domains. Despite the usage of …
Anomaly detection in time series: a comprehensive evaluation
S Schmidl, P Wenig, T Papenbrock - Proceedings of the VLDB …, 2022 - dl.acm.org
Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …
ranging from manufacturing processes over finance applications to health care monitoring …
Fraud detection in financial statements using data mining and GAN models
Financial statements are analytical reports published periodically by financial institutions
explaining their performance from different perspectives. As these reports are the …
explaining their performance from different perspectives. As these reports are the …
Deeprest: deep resource estimation for interactive microservices
Interactive microservices expose API endpoints to be invoked by users. For such
applications, precisely estimating the resources required to serve specific API traffic is …
applications, precisely estimating the resources required to serve specific API traffic is …
Forecasting network events to estimate attack risk: Integration of wavelet transform and vector auto regression with exogenous variables
Analyzing network traffic data to detect suspicious network activities (ie, intrusions) requires
tremendous effort due to the variability of the data and constant changes in network traffic …
tremendous effort due to the variability of the data and constant changes in network traffic …
ReRe: A lightweight real-time ready-to-go anomaly detection approach for time series
MC Lee, JC Lin, EG Gan - 2020 IEEE 44th Annual Computers …, 2020 - ieeexplore.ieee.org
Anomaly detection is an active research topic in many different fields such as intrusion
detection, network monitoring, system health monitoring, IoT healthcare, etc. However, many …
detection, network monitoring, system health monitoring, IoT healthcare, etc. However, many …
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series
MC Lee, JC Lin - arXiv preprint arXiv:2303.00409, 2023 - arxiv.org
An open-ended time series refers to a series of data points indexed in time order without an
end. Such a time series can be found everywhere due to the prevalence of Internet of …
end. Such a time series can be found everywhere due to the prevalence of Internet of …
RoLA: A real-time online lightweight anomaly detection system for multivariate time series
MC Lee, JC Lin - arXiv preprint arXiv:2305.16509, 2023 - arxiv.org
A multivariate time series refers to observations of two or more variables taken from a device
or a system simultaneously over time. There is an increasing need to monitor multivariate …
or a system simultaneously over time. There is an increasing need to monitor multivariate …
AREP: an adaptive, machine learning-based algorithm for real-time anomaly detection on network telemetry data
K Farkas - Neural Computing and Applications, 2023 - Springer
Abnormal behaviour detection is an essential task of real-time monitoring to secure the
reliable operation of ICT infrastructures. This paper presents AREP, an adaptive, long short …
reliable operation of ICT infrastructures. This paper presents AREP, an adaptive, long short …