Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the
emerging Internet of Things (IoT) has gained a lot of attention from the government …

The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans
and envisioned the concept of smart cities using informed actions, enhanced user …

The Internet of Things (IoT) is made up of billions of physical devices connected to the
Internet via networks that perform tasks independently with less human intervention. Such …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Abstract The Internet of Things (IoT) is a paradigm that connects objects to the Internet as a
whole and enables them to work together to achieve common objectives, such as innovative …

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 …

Federated learning (FL) is a recent development in artificial intelligence, which is typically
based on the concept of decentralized data. As cyberattacks are frequently happening in the …

In this paper, we propose a federated learning-based intrusion detection system, named
FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects …

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 …