The rapid development of network communication along with the drastic increase in the
number of smart devices has triggered a surge in network traffic, which can contain private …

Many of the proposed machine learning (ML) based network intrusion detection systems
(NIDSs) achieve near perfect detection performance when evaluated on synthetic …

Network intrusion detection methods based on federated learning (FL) and edge computing
have great potential for protecting the cybersecurity of the Internet of Things. It overcomes …

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …

Federated Learning (FL) with mobile computing and the Internet of Things (IoT) is an
effective cooperative learning approach. However, several technical challenges still need to …

Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

Threat hunting is the action of seeking harmful actors lurking in the network or the system in
the early stage with the assumption of attackers already broke the cy-ber defense solution …

In the modern interconnected world, intelligent networks and computing technologies are
increasingly being incorporated in industrial systems. However, this adoption of advanced …

Existing generative adversarial networks (GANs), primarily used for creating fake image
samples from natural images, demand a strong dependence (ie, the training strategy of the …

Billions of IoT devices lacking proper security mechanisms have been manufactured and
deployed for the last years, and more will come with the development of Beyond 5G …