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

Network entities have important asset mapping, vulnerability, and service delivery
applications. In cyberspace, where the network structure is complex and the number of …

Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of
smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of …

Graph neural network-based network intrusion detection systems have recently
demonstrated state-of-the-art performance on benchmark datasets. Nevertheless, these …

Abstract Graph Neural Networks (GNNs) have garnered intensive attention for Network
Intrusion Detection System (NIDS) due to their suitability for representing the network traffic …

Neural network architectures have been used to address multiple real-world problems with
high success. Their extension to graph-structured data started recently to be explored …

As network sizes grow, attack schemes not only become more varied but also increase in
complexity. This diversification leads to a proliferation of attack variants, complicating the …

One of the critical technologies to ensure cyberspace security is network traffic anomaly
detection, which detects malicious attacks by analyzing and identifying network traffic …

With the expansion of the Internet, Internet of Things devices, and related services, effective
intrusion detection systems are vital in cybersecurity. This study presents a significant …

Recent works have demonstrated the potential of Graph Neural Networks (GNN) for network
intrusion detection. Despite their advantages, a significant gap persists between real-world …