The Industrial Internet of Things (IIoT) plays an important role in digital transformation of
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …

This paper presents a new Network Intrusion Detection System (NIDS) based on Graph
Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks …

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 …

The high volume of increasingly sophisticated cyber threats is drawing growing attention to
cybersecurity, where many challenges remain unresolved. Namely, for intrusion detection …

Early and accurate detection of network intrusions is crucial to ensure network security and
stability. Existing network intrusion detection methods mainly use conventional machine …

With the proliferation of the Internet, network complexities for both commercial and state
organizations have significantly increased, leading to more sophisticated and harder-to …

Anomaly detection is critical across domains, from cybersecurity to fraud prevention. Graphs,
adept at modeling intricate relationships, offer a flexible framework for capturing complex …

Including high-dimensional categorical predictors in a machine learning model is a major
challenge. This is particularly appropriate for the IP and Port addresses of network …

With the flood of different attacks in the network environment, the Network-based Intrusion
Detection System (NIDS) has become an important tool to ensure information security. The …

Artificial intelligence (AI) approaches are widely applied in cyber security, while they
currently lack explainability towards their users. Here, complex network analysis (CNA) can …