Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

The development of internet technology has brought us benefits, but at the same time, there
has been a surge in network attack incidents, posing a serious threat to network security. In …

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

Abstract The Internet of Things (IoT) connects daily use devices to the Internet, such as
home appliances, health care equipment, sensors, and industrial devices. Concurrently …

Abstract The Internet of Things (IoT) is a vast network of devices with sensors or actuators
connected through wired or wireless networks. It has a transformative effect on integrating …

In network intrusion detection, graph neural networks (GNNs) have gained remarkable
attention in addressing cybersecurity threats. This research addresses the growing …

In recent years, graph convolutional networks (GCNs) have gained widespread attention
and applications in image classification tasks. While traditional convolutional neural …

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 …

This research introduces a novel framework utilizing a sequential gated graph convolutional
neural network (GGCN) designed specifically for botnet detection within Internet of Things …