TemporalFED: Detecting cyberattacks in industrial time-series data using decentralized federated learning
Industry 4.0 has brought numerous advantages, such as increasing productivity through
automation. However, it also presents major cybersecurity issues such as cyberattacks …
automation. However, it also presents major cybersecurity issues such as cyberattacks …
[HTML][HTML] FSL: federated sequential learning-based cyberattack detection for Industrial Internet of Things
F Li, J Lin, H Han - Industrial Artificial Intelligence, 2023 - Springer
Abstract Industrial Internet of Things (IIoT) brings revolutionary technical supports to modern
industries. However, today's IIoT still faces the challenges of modeling varying time-series in …
industries. However, today's IIoT still faces the challenges of modeling varying time-series in …
Light-weight federated learning-based anomaly detection for time-series data in industrial control systems
With the emergence of the Industrial Internet of Things (IIoT), potential threats to smart
manufacturing systems are increasingly becoming challenging, causing severe damage to …
manufacturing systems are increasingly becoming challenging, causing severe damage to …
Deep federated anomaly detection for multivariate time series data
Although many anomaly detection approaches have been developed for multivariate time
series data, limited effort has been made in federated settings in which multivariate time …
series data, limited effort has been made in federated settings in which multivariate time …
Federated Temporal Learning Based Cyber Attack Detection for Distributed Industrial IoT Systems
J Lin, F Li, H Han - 2023 CAA Symposium on Fault Detection …, 2023 - ieeexplore.ieee.org
Industrial Internet of Things (IIoT) is widely applied in modern industries. However, IIoT faces
challenges in system modeling and cyber security. Distributed data isolation influences the …
challenges in system modeling and cyber security. Distributed data isolation influences the …
FedTADBench: Federated time-series anomaly detection benchmark
Time series anomaly detection strives to uncover potential abnormal behaviors and patterns
from temporal data, and has fundamental significance in diverse application scenarios …
from temporal data, and has fundamental significance in diverse application scenarios …
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 …
Detecting cyberattacks using anomaly detection in industrial control systems: A federated learning approach
In recent years, the rapid development and wide application of advanced technologies have
profoundly impacted industrial manufacturing, leading to smart manufacturing (SM) …
profoundly impacted industrial manufacturing, leading to smart manufacturing (SM) …
Communication-efficient federated learning for anomaly detection in industrial internet of things
With the rapid development of the Industrial Internet of Things (IIoT), various IoT devices and
sensors generate massive industrial sensing data. Sensing big data can be analyzed for …
sensors generate massive industrial sensing data. Sensing big data can be analyzed for …
[HTML][HTML] Enhancing IoT anomaly detection performance for federated learning
Federated Learning (FL) with mobile computing and the Internet of Things (IoT) is an
effective cooperative learning approach. However, several technical challenges still need to …
effective cooperative learning approach. However, several technical challenges still need to …