Intrusion detection through classifying incoming packets is a crucial functionality at the
network edge, requiring accuracy, efficiency and scalability at the same time, introducing a …

Predominant network intrusion detection systems (NIDS) aim to identify malicious traffic
patterns based on a handcrafted dataset of rules. Recently, the application of machine …

Deploying accurate machine learning algorithms into a high-throughput networking
environment is a challenging task. On the one hand, machine learning has proved itself …

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 …

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

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 …

In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative
Machine Learning (ML). FL does not share local data but ML models, offering applications in …

Traditional machine learning models used for network intrusion detection systems rely on
vast amounts of network traffic data with expertly engineered features. The abundance of …

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 …

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