Deep learning based intelligent intrusion detection (IID) methods have been received
strongly attention for computer security protection in cybersecurity. All these learning models …

Data centralization can potentially increase Internet of Things (IoT) usage. The trend is to
move IoT devices to a centralized server with higher memory capacity and a more robust …

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 …

The Internet of Things (IoT) is a network of electrical devices that are connected to the
Internet wirelessly. This group of devices generates a large amount of data with information …

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 …

The rise in computer networks and internet attacks has become alarming for most service
providers. It has triggered the need for the development and implementation of intrusion …

With the increase and diversity of network attacks, machine learning has shown its efficiency
in realizing intrusion detection. Federated Learning (FL) has been proposed as a new …

Abstract Machine learning techniques have been widely used in intrusion detection for many
years. However, these techniques are still suffer from lack of labeled dataset, heavy …

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 …

The volume of network and Internet traffic is expanding daily, with data being created at the
zettabyte to petabyte scale at an exceptionally high rate. These can be characterized as big …