The introduction of the Internet of Things has led to the connectivity of millions of devices
with less human interaction. This demand in connectivity has resulted in a surge in network …

This study presents a new intrusion detection system (IDS) for Wireless Ad hoc Networks,
leveraging graph neural networks (GNN). Overcoming the challenges faced by traditional …

In network defence, intrusion detection is crucial to identify malicious activities such as
attacks, intrusions, and malware. Intrusion Detection Systems (IDSs) are mandatory for …

Wireless Sensor Networks (WSNs) are the key underlying technology of the Internet of
Things (IoT); however, these networks are energy constrained. Security has become a major …

Real-time deep learning faces the challenge of balancing accuracy and time, especially in
cybersecurity where intrusion detection is crucial. Traditional deep learning techniques have …

Delivering goods is crucial to the supply chain industry because it directly affects package
delivery, a crucial aspect of real-time vehicle movement on which most e-commerce …

Previous research on fraud detection modeling is often based on a single algorithm,
optimizing categories and clusters to find fraudulent patterns that they have provided …

Due to its distinct production paradigm, additive manufacturing (AM) is positioned to bring
about a revolution. It presents the possibility of on-demand, decentralized, and mass …

The number of cyberattacks has grown along with the expansion of the Internet of Things
(IoT), which necessitates detection of cyberattacks on IoT devices. Different machine …

Adaptive online machine learning using data streams is an emerging research area in which
algorithms learn dynamically from live data and update regularly for future predictions. On …