IoT offers capabilities to gather information from digital devices, infer from their results, and
maintain and optimize these devices in different domains. IoT is heterogeneous in nature …

The rapid development of the Internet and smart devices trigger surge in network traffic
making its infrastructure more complex and heterogeneous. The predominated usage of …

Deep learning (DL) has been widely proposed for botnet attack detection in Internet of
Things (IoT) networks. However, the traditional centralized DL (CDL) method cannot be …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

In this article, we present a comprehensive study with an experimental analysis of federated
deep learning approaches for cyber security in the Internet of Things (IoT) applications …

With the advance of machine learning and the Internet of Things (IoT), security and privacy
have become critical concerns in mobile services and networks. Transferring data to a …

Abstract The application of Machine Learning (ML) techniques to the well-known intrusion
detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks …

Federated learning (FL) has attracted significant interest given its prominent advantages and
applicability in many scenarios. However, it has been demonstrated that sharing updated …

Privacy has emerged as a top worry as a result of the development of zero-day hacks
because IoT devices produce and transmit sensitive information through the regular internet …

The industrial internet of things (IIoT) is an evolutionary extension of the traditional Internet of
Things (IoT) into processes and machines for applications in the industrial sector. The IIoT …