Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

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 …

Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

Deep neural networks (DNNs) provide excellent performance across a wide range of
classification tasks, but their training requires high computational resources and is often …

In security-sensitive applications, the success of machine learning depends on a thorough
vetting of their resistance to adversarial data. In one pertinent, well-motivated attack …

Despite decades of research in network traffic analysis and incredible advances in artificial
intelligence, network intrusion detection systems based on machine learning (ML) have yet …

The dependency of our society on networked computers has become frightening: In the
economy, all-digital networks have turned from facilitators to drivers; as cyber-physical …

Pattern classification systems are commonly used in adversarial applications, like biometric
authentication, network intrusion detection, and spam filtering, in which data can be …

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 …