Traffic classification is essential for network management and optimization, enhancing user
experience, network performance, and security. However, evolving technologies and …

The Internet of Things (IoT) has revolutionized our lives by generating large amounts of data,
however, the data needs to be collected, processed, and analyzed in real-time. Network …

This paper explores the comparative analysis of federated learning (FL) and centralized
learning (CL) models in the context of multi-class traffic flow classification for network …

Federated learning (FL) is an innovative distributed learning paradigm that permits multiple
parties to train models collaboratively while protecting individual privacy. However, it …

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber
crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are …

Robust machine learning (ML) models can be developed by leveraging large volumes of
data and distributing the computational tasks across numerous devices or servers …

The increasing prevalence of botnet attacks in IoT networks has led to the development of
deep learning techniques for their detection. However, conventional centralized deep …

Network Traffic Classification (NTC) serves as a crucial element in network management,
and the rapid progress in machine learning has inspired the utilization of learning methods …

Robust machine learning (ML) models can be developed by leveraging large volumes of
data and distributing the computational tasks across numerous devices or servers …

The exponential growth of internet traffic causes significant challenges for network traffic
classification, such as maintaining data privacy and requiring more computing resources. To …