Nowadays, the ever-increasing complication and severity of security attacks on computer
networks have inspired security researchers to incorporate different machine learning …

This study reviews and analyses the research landscape for intrusion detection systems
(IDSs) based on deep learning (DL) techniques into a coherent taxonomy and identifies the …

The massive growth of data that are transmitted through a variety of devices and
communication protocols have raised serious security concerns, which have increased the …

Abstract The Internet of Things era is being replaced by the Internet of Everything (IoE) era,
where everything can communicate with everything else. With the advent of the fourth …

Introduction: The growth of ubiquitous networked devices and the proliferation of
geographically dispersed 'Internet of Thing'devices have exponentially increased network …

In this investigation, we apply deep learning to generate a desirable data generation model
which helps to balance the network intrusion detection datasets and enhance the detection …

Abstract The Internet of Things (IoT) connects billions of interconnected devices that can
exchange information with each other with minimal user intervention. The goal of IoT is to …

Data imbalance in network intrusion detection datasets tends to incur underfitting or
deviation in classifier training. This investigation applies Batched Variational AutoEncoders …

The ever-increasing complication and severity of the computer networks' security attacks
have inspired security researchers to apply various machine learning methods to protect the …

Technology is embedded in society. Systems/networks protection is paramount, vigilance
against intrusions/attacks set to disrupt. Distributed Denial of Service (DDoS) attacks are …