LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT
The data generated by millions of sensors in the industrial Internet of Things (IIoT) are
extremely dynamic, heterogeneous, and large scale and pose great challenges on the real …
extremely dynamic, heterogeneous, and large scale and pose great challenges on the real …
Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach
Since edge device failures (ie, anomalies) seriously affect the production of industrial
products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming …
products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming …
IoT data analytics in dynamic environments: From an automated machine learning perspective
With the wide spread of sensors and smart devices in recent years, the data generation
speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems …
speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems …
Effectively detecting operational anomalies in large-scale IoT data infrastructures by using a GAN-based predictive model
Quality of data services is crucial for operational large-scale internet-of-things (IoT) research
data infrastructure, in particular when serving large amounts of distributed users. Effectively …
data infrastructure, in particular when serving large amounts of distributed users. Effectively …
Anomaly detection in industrial IoT using distributional reinforcement learning and generative adversarial networks
Anomaly detection is one of the biggest issues of security in the Industrial Internet of Things
(IIoT) due to the increase in cyber attack dangers for distributed devices and critical …
(IIoT) due to the increase in cyber attack dangers for distributed devices and critical …
An autocorrelation-based LSTM-autoencoder for anomaly detection on time-series data
Data quality significantly impacts the results of data analytics. Researchers have proposed
machine learning based anomaly detection techniques to identify incorrect data. Existing …
machine learning based anomaly detection techniques to identify incorrect data. Existing …
Real-time deep anomaly detection framework for multivariate time-series data in industrial iot
H Nizam, S Zafar, Z Lv, F Wang, X Hu - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
The data produced by millions of connected devices and smart sensors in the Industrial
Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped …
Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped …
A review of machine learning and deep learning techniques for anomaly detection in IoT data
R Al-amri, RK Murugesan, M Man, AF Abdulateef… - Applied Sciences, 2021 - mdpi.com
Anomaly detection has gained considerable attention in the past couple of years. Emerging
technologies, such as the Internet of Things (IoT), are known to be among the most critical …
technologies, such as the Internet of Things (IoT), are known to be among the most critical …
[HTML][HTML] An ensemble deep learning model for cyber threat hunting in industrial internet of things
By the emergence of the fourth industrial revolution, interconnected devices and sensors
generate large-scale, dynamic, and inharmonious data in Industrial Internet of Things (IIoT) …
generate large-scale, dynamic, and inharmonious data in Industrial Internet of Things (IIoT) …
Unsupervised online anomaly detection on multivariate sensing time series data for smart manufacturing
RJ Hsieh, J Chou, CH Ho - 2019 IEEE 12th conference on …, 2019 - ieeexplore.ieee.org
The emergence of IoT and AI has brought revolutionary change in various application
domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to …
domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to …