Federated learning (FL) provides a distributed machine learning system that enables
participants to train using local data to create a shared model by eliminating the requirement …

The ramifications of the COVID-19 pandemic have contributed in part to a recent upsurge in
the study and development of eHealth systems. Although it is almost impossible to cover all …

The continuous emergence of new and sophisticated malware specifically targeting Android-
based Internet of Things devices is causing significant security hazards and is consequently …

Intrusion detection systems are evolving into intelligent systems that perform data analysis
searching for anomalies in their environment. The development of deep learning …

Over the past five years, interest in the literature regarding the security of the Internet of
Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT …

Network intrusion detection systems are evolving into intelligent systems that perform data
analysis while searching for anomalies in their environment. Indeed, the development of …

For healthcare datasets, it is often impossible to combine data samples from multiple sites
due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of …

Federated learning holds great potential for enabling large-scale healthcare research and
collaboration across multiple centres while ensuring data privacy and security are not …

Fingerprint recognition has been a vital security guard for various applications whose
vulnerability has been explored by different works. However, previous works on spoofing …

Over the last 20 years, Wi-Fi technology has advanced to the point where most modern
devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely …