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 …

Network intrusion detection is used to detect unauthorized activities on a digital network,
with which the cybersecurity teams of organizations can then kick-start prevention protocols …

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 …

Detecting anomalies, intrusions, and security threats in the network (including Internet of
Things) traffic necessitates the processing of large volumes of sensitive data, which raises …

In this paper, we present FLUIDS, a Federated Learning with semi-sUpervised approach for
Intrusion Detection System. FLUIDS formulates the intrusion detection into a semi …

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

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

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 …

To deal with the increasing number of cyber-attacks, intrusion detection system (IDS) plays
an important role in monitoring and ensuring the security of the computer network. With the …

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 …