Network intrusion detection using a hybrid graph-based convolutional network and transformer architecture.

ABSTRACT: Cloud computing continues to expand rapidly due to its ability to provide internet-hosted services, including servers, databases, and storage. However, this growth increases exposure to sophisticated intrusion attacks that can evade traditional security mechanisms such as firewalls. As a result, network intrusion detection systems (NIDS) enhanced with machine learning and deep learning have become increasingly important. Despite notable advancements, many AI-based intrusion detection models remain limited by their dependence on extensive, high-quality attack datasets and their insufficient capacity to capture complex, dynamic patterns in distributed cloud environments. This study presents a hybrid intrusion detection model that combines a graph convolutional layer and a transformer encoder

SUBMITTER: Appiahene P 

PROVIDER: S-EPMC12822977 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature