CNN-based adversarial machine learning models are proposed to drive the innovation of
anomaly detection techniques under Industry 5.0. However, the generalization inherent in …

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 …

For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to
build smart industry. Especially, explosive time-series data pose enormous challenges to the …

Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse
disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of …

Anomaly detection for industrial diesel generators, in which unexpected faults could lead to
severe consequences, is still challenged due to their complex structure and nonstationary …

Whether in the realm of software or hardware, datasets representing the state of systems are
mostly imbalanced. This imbalance is because these systems' reliability requirements make …

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) …

Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by
many fields, including intrusion detection, device-activity analysis, and security supervision …

With the rapid development of communication net-work, the types and quantities of network
traffic data have in-creased substantially. What followed was the frequent occurrence of …

Despite the superior performance in modeling complex patterns to address challenging
problems, the black-box nature of Deep Learning (DL) methods impose limitations to their …