Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …
based on deep learning have been proposed for the analysis of multivariate time series. In …
[HTML][HTML] IoT anomaly detection methods and applications: A survey
A Chatterjee, BS Ahmed - Internet of Things, 2022 - Elsevier
Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly
expanding field. This growth necessitates an examination of application trends and current …
expanding field. This growth necessitates an examination of application trends and current …
Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation
Y Xu, C Hu, Q Wu, S Jian, Z Li, Y Chen, G Zhang… - Journal of …, 2022 - Elsevier
Flood forecasting is an essential non-engineering measure for flood prevention and disaster
reduction. Many models have been developed to study the complex and highly random …
reduction. Many models have been developed to study the complex and highly random …
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 …
Machine learning for wireless sensor networks security: An overview of challenges and issues
Energy and security are major challenges in a wireless sensor network, and they work
oppositely. As security complexity increases, battery drain will increase. Due to the limited …
oppositely. As security complexity increases, battery drain will increase. Due to the limited …
Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0
Various cyber attacks often occur in logistics network of the Industry 4.0, which poses a
threat to Internet security. Intrusion detection can intelligently detect anomalous activities …
threat to Internet security. Intrusion detection can intelligently detect anomalous activities …
[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) …
LSTM-autoencoder-based anomaly detection for indoor air quality time-series data
Anomaly detection for indoor air quality (IAQ) data has become an important area of
research as the quality of air is closely related to human health and well-being. However …
research as the quality of air is closely related to human health and well-being. However …
Toward accurate anomaly detection in industrial internet of things using hierarchical federated learning
The Industrial Internet of Things (IIoT) is an emerging technology that can promote the
development of industrial intelligence, improve production efficiency, and reduce …
development of industrial intelligence, improve production efficiency, and reduce …
Deep-IFS: Intrusion detection approach for industrial internet of things traffic in fog environment
M Abdel-Basset, V Chang, H Hawash… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
The extensive propagation of industrial Internet of Things (IIoT) technologies has
encouraged intruders to initiate a variety of attacks that need to be identified to maintain the …
encouraged intruders to initiate a variety of attacks that need to be identified to maintain the …