Along with the popularity of the Internet of Things (IoT) techniques with several
computational paradigms, such as cloud and edge computing, microservice has been …

Due to the high dependency of traditional intrusion detection method on a fully-labeled large
dataset, existing works can hardly be applied in real-world scenarios, especially facing zero …

With the increasing population of Industry 4.0, both AI and smart techniques have been
applied and become hotly discussed topics in industrial cyber-physical systems (CPS) …

Network intrusion detection is an essential means to ensure the security of the network
information system. In the real network, abnormal behaviors occur much less frequently than …

Conventional intrusion detection systems based on supervised learning techniques require
a large number of samples for training, while in some scenarios, such as zero-day attacks …

Our generation has seen the boom and ubiquitous advent of Internet connectivity.
Adversaries have been exploiting this omnipresent connectivity as an opportunity to launch …

Although few-shot learning and one-class classification (OCC), ie, learning a binary
classifier with data from only one class, have been separately well studied, their intersection …

The techniques of machine learning, as well as deep learning (DL) methods, have seen a
wide application in the intelligent fault diagnosis field these years. However, contemporary …

Industrial Internet of Things (IIoT) has brought a lot of convenience for the industrial world to
digitization, automation, and intelligence, but it inevitably introduces inherent cyber security …

Cyber attacks and intrusions have become the major obstacles to the adoption of the
Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a …