Communication networks are important infrastructures in contemporary society. There are
still many challenges that are not fully solved and new solutions are proposed continuously …

The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …

In recent years, many methods for intrusion detection systems (IDS) have been designed
and developed in the research community, which have achieved a perfect detection rate …

This paper investigates graph neural networks (GNNs) applied for self-supervised intrusion
and anomaly detection in computer networks. GNNs are a deep learning approach for graph …

Network intrusion detection systems (NIDS) play a crucial role in maintaining network
security. However, current NIDS techniques tend to neglect the topological structures of …

Deep learning (DL) greatly enhances binary anomaly detection capabilities through effective
statistical network characterization; nevertheless, the intrusion class differentiation …

Nowadays, hackers take illegal advantage of distributed resources in a network of
computing devices (ie, botnet) to launch cyberattacks against the Internet of Things (IoT) …

Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …

Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based
data model, provide holistic approaches for processing massive volumes of complex …

With recent advances in the Internet of Things (IoT) technology, more people can have
instant and easy access to the IoT network of vast and diverse interconnected devices (eg …