Anomaly detection for IoT time-series data: A survey
Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …
the identification of novel or unexpected observations or sequences within the data being …
Anomaly detection, analysis and prediction techniques in iot environment: A systematic literature review
M Fahim, A Sillitti - IEEE Access, 2019 - ieeexplore.ieee.org
Anomaly detection has attracted considerable attention from the research community in the
past few years due to the advancement of sensor monitoring technologies, low-cost …
past few years due to the advancement of sensor monitoring technologies, low-cost …
Tsmae: a novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder
With the development of communication, the Internet of Things (IoT) has been widely
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …
[HTML][HTML] An unsupervised TinyML approach applied to the detection of urban noise anomalies under the smart cities environment
Abstract Artificial Intelligence of Things (AIoT) is an emerging area of interest, and this can
be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) …
be used to obtain knowledge and take better decisions in the same Internet of Things (IoT) …
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 …
Weather data analysis and sensor fault detection using an extended IoT framework with semantics, big data, and machine learning
In recent years, big data and Internet of Things (IoT) implementations started getting more
attention. Researchers focused on develo** big data analytics solutions using machine …
attention. Researchers focused on develo** big data analytics solutions using machine …
Analysis of intruder detection in big data analytics
Network security and data security is gaining vital importance as the usage of applications
on computer networks evolving now-a-days. Tremendous increase of data usage on real …
on computer networks evolving now-a-days. Tremendous increase of data usage on real …
Human-computer interaction with big data analytics
Big Data has been playing a vital role in almost all environments such as healthcare,
education, business organizations and scientific research. Big data analytics requires …
education, business organizations and scientific research. Big data analytics requires …
[PDF][PDF] Anomaly detection in computer networks: A state-of-the-art review.
SWAH Baddar, A Merlo, M Migliardi - J. Wirel. Mob. Networks …, 2014 - jowua.com
The ever-lasting challenge of detecting and mitigating failures in computer networks has
become more essential than ever; especially with the enormous number of smart devices …
become more essential than ever; especially with the enormous number of smart devices …
Explainable contextual anomaly detection using quantile regression forests
Z Li, M Van Leeuwen - Data Mining and Knowledge Discovery, 2023 - Springer
Traditional anomaly detection methods aim to identify objects that deviate from most other
objects by treating all features equally. In contrast, contextual anomaly detection methods …
objects by treating all features equally. In contrast, contextual anomaly detection methods …