In network traffic classification, it is important to understand the correlation between network
traffic and its causal application, protocol, or service group, for example, in facilitating lawful …

The adoption of network traffic encryption is continually growing. Popular applications use
encryption protocols to secure communications and protect the privacy of users. In addition …

Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …

Traffic characterization is one of the major challenges in today's security industry. The
continuous evolution and generation of new applications and services, together with the …

This survey paper describes the significant literature survey of Sustainable Smart Cities
(SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection …

Mobile-application fingerprinting of network traffic is valuable for many security solutions as
it provides insights into the apps active on a network. Unfortunately, existing techniques …

Traffic classification groups similar or related traffic data, which is one main stream
technique of data fusion in the field of network management and security. With the rapid …

Machine-learning (ML) enables computers to learn how to recognise patterns, make
unintended decisions, or react to a dynamic environment. The effectiveness of trained …

Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …