Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

With the emergence of the Internet-of-Things (IoT) and seamless Internet connectivity, the
need to process streaming data on real-time basis has become essential. However, the …

The continuous development and usage of multi-media-based applications and services
have contributed to the exponential growth of social multimedia traffic. In this context, secure …

Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …

Learning-based classifiers are increasingly used for detection of various forms of malicious
data. However, if they are deployed online, an attacker may attempt to evade them by …

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem
from adversarial or unlikely events with unknown distributions. However, the predictive …

The emergence of Internet connectivity has led to a significant increase in the volume and
complexity of cyber attacks. Abnormal behavior detection systems are valuable tools for …

As we head towards the IoT (Internet of Things) era, protecting network infrastructures and
information security has become increasingly crucial. In recent years, Anomaly‐Based …

With the rapid rise in the ubiquity and sophistication of Internet technology and the
accompanying growth in the number of network attacks, network intrusion detection has …