Federated Learning (FL) has been recently receiving increasing consideration from the
cybersecurity community as a way to collaboratively train deep learning models with …

Recently, federated learning has been used by Network Intrusion Detection Systems
(NIDSs) to expanding data features while preserving data privacy. However, non …

Recently, DDoS attack has developed rapidly and become one of the most important threats
to the Internet. Traditional machine learning and deep learning methods can-not train a …

Distributed Denial-of-Service (DDoS) attacks are expected to continue plaguing service
availability in emerging networks which rely on distributed edge clouds to offer critical …

Low-rate Distributed Denial of Service (LDDoS) attacks have been one of the most notorious
network security threats, which use periodic slight multi-variate time series pulse flows to …

Billions of IoT devices are connected to networks all around us, enabling cyber-physical
systems. These devices can carry and generate user-sensitive data, examples of such …

Federated Learning (FL) has emerged as a powerful paradigm for training Machine
Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while …

Network intrusion detection systems are evolving into intelligent systems that perform data
analysis while searching for anomalies in their environment. Indeed, the development of …

Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …

We are attending a severe zero-day cyber attacks. Machine learning based anomaly
detection is definitely the most efficient defence in depth approach. It consists to analyzing …