The development of autonomous or self-driving networks is one of the main challenges
faced by the telecommunication industry. Future networks are expected to realise a number …

With the significant increase in cyber-attacks and attempts to gain unauthorised access to
systems and information, Network Intrusion-Detection Systems (NIDSs) have become …

Recent network traffic classification methods benefit from machine learning (ML) technology.
However, there are many challenges due to the use of ML, such as lack of high-quality …

Abstract Machine learning is recognised as a relevant approach to detect attacks and other
anomalies in network traffic. However, there are still no suitable network datasets that would …

Recent network traffic classification methods benefitfrom machine learning (ML) technology.
However, there aremany challenges due to use of ML, such as: lack of high …

Intelligent and autonomous networks require precise and fast mechanisms that ensure error-
free and efficient operation. Modern solutions are increasingly based on artificial …

This study investigates the evolving landscape of network traffic monitoring, which is crucial
for maintaining computer network services and security. Traditional methods like Deep …

High-quality datasets are an essential requirement for leveraging machine learning (ML) in
data processing and recently in network security as well. However, the quality of datasets is …

S rostoucím objemem šifrovaného provozu v síti narůstá potřeba korektní identifikace a
monitorování tohoto provozu. Pro řešení toho problému se využívá algoritmů strojového …

V oblasti síťové bezpečnosti se používají techniky strojového učení pro efektivní detekci
anomálií a malwaru v síťovém provozu. Pro natrénování síťového klasifikátoru s vysokou …