The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

Emerging technologies drive the ongoing transformation of Intelligent Transportation
Systems (ITS). This transformation has given rise to cybersecurity concerns, among which …

Machine learning models have been widely adopted in several fields. However, most recent
studies have shown several vulnerabilities from attacks with a potential to jeopardize the …

The advent of sixth-generation (6G) networks is expected to start a new era in mobile
networks, characterized by unprecedented high demands on dense connectivity, ultra …

Federated learning (FL) enables multiple worker devices share local models trained on their
private data to collaboratively train a machine learning model. However, local models are …

Although crowdsensing has emerged as a popular information collection paradigm, its
security and privacy vulnerabilities have come to the forefront in recent years. However, one …

Connected and autonomous vehicles (CAVs) can fulfill the emerging demand for smart
transportation on a global scale. Such innovations for transportation can bring manyfold …

In smart grid services, electricity consumption data sharing may lead to privacy breaches
and the potential for misuse of personal data. Existing data aggregation approaches mainly …

Semi-supervised encrypted traffic anomaly detection models in zero-positive scenarios are
susceptible to human labeling errors or poisoning attacks, thereby compromising the …

Multi-user semantic communication (MUSC) has emerged as a promising paradigm for
future 6G networks and applications, where massive clients (eg, mobile devices) …