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

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 …

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 …

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 …

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 become an increasingly popular solution for intrusion detection
to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based …

Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …

The growing reliance of industry 4.0/5.0 on emergent technologies has dramatically
increased the scope of cyber threats and data privacy issues. Recently, federated learning …

Intrusion detection is a common network security defense technology. At present, there are
many research using deep learning to realize network intrusion detection. This method has …