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 …

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 …

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 …

In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative
Machine Learning (ML). FL does not share local data but ML models, offering applications in …

The development and popularity of vehicle-to-everything communication have caused more
risks to the in-vehicle networks security. As a result, an increasing number of various and …

Abstract Machine Learning techniques for network-based intrusion detection are widely
adopted in the scientific literature. Besides being highly variable, network traffic behavior …

Federated learning (FL) is a type of distributed machine learning approacs that trains global
models through the collaboration of participants. It protects data privacy as participants only …

Multi-source domain adaptation has been intensively studied. The distribution shift in
features inherent to specific domains causes the negative transfer problem, degrading a …

Small-and medium-sized enterprises (SMEs) frequently experience cyberattacks, but often
do not have the means to counter these attacks. Therefore, cybersecurity researchers and …