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 …

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 …

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 …

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 …

Intrusion detection systems are evolving into intelligent systems that perform data analysis
searching for anomalies in their environment. The development of deep learning …

In order to provide an accurate and timely response to different types of the attacks, intrusion
and anomaly detection systems collect and analyze a lot of data that may include personal …

The constantly evolving digital transformation imposes new requirements on our society.
Aspects relating to reliance on the networking domain and the difficulty of achieving security …

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 …