The impact of Internet of Things (IoT) has become increasingly significant in smart
manufacturing, while deep generative model (DGM) is viewed as a promising learning …

As communication technology advances, various and heterogeneous data are
communicated in distributed environments through network systems. Meanwhile, along with …

Detecting a large number of attack classes accurately applying machine learning (ML) and
deep learning (DL) techniques depends on the number of representative samples available …

The expeditious growth of the World Wide Web and the rampant flow of network traffic have
resulted in a continuous increase of network security threats. Cyber attackers seek to exploit …

Class imbalance is a critical issue in customer classification, for which a plethora of
techniques have been proposed in the current body of literature. In particular, generative …

While anomaly detection and the related concept of intrusion detection are widely studied,
detecting anomalies in new operating behavior in environments such as the Internet of …

Cyber-attack is one of the alarming issues in today's era. Firewalls, Intrusion Detection
Systems (IDSs), and other techniques are popularly applied to prevent those attacks …

Network intrusion detection is an important technology for maintaining cybersecurity. The
inherent difficulties co-existing in network traffic datasets, such as class imbalance, class …

Intrusion detection systems (IDSs) investigate various attacks, identify malicious patterns,
and implement effective control strategies. With the recent advances in machine learning …

In this work, the problem of anomaly detection in imbalanced datasets, framed in the context
of network intrusion detection is studied. A novel anomaly detection solution that takes both …