Deep learning has seen significant growth recently and is now applied to a wide range of
conventional use cases, including graphs. Graph data provides relational information …

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 …

Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …

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 …

The last few years have seen a high volume of sophisticated cyber-attacks leading to
financial instability and privacy breaches. This reveals the need for a Network Intrusion …

Over the past few years, massive penetrations targeting an Industrial Control System (ICS)
network intend to compromise its core industrial processes. So far, numerous advanced …

This paper presents a new Android malware de-tection method based on Graph Neural
Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) …

We propose an attention-weighted model for parallel extraction of spatial-temporal features
to enhance the detection capabilities in the message queuing telemetry transport protocol …

As the complexity and scale of modern networks continue to grow, the need for efficient,
secure management, and optimization becomes increasingly vital. Digital twin (DT) …

Abstract Background and Objectives Graph neural networks (GNN) have demonstrated
remarkable encoding capabilities in the context of brain network classification tasks. They …